文章详情页增加生成produce 的按钮
This commit is contained in:
@@ -1569,6 +1569,8 @@ const en = {
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cancel: 'Cancel'
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},
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refRelevance: {
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startDetect: 'Start relevance check',
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detecting: 'Detecting…',
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filterAll: 'All ({count})',
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filterModify: 'Needs revision ({count})',
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columnTitle: 'AI citation relevance review',
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@@ -1550,6 +1550,8 @@ const zh = {
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cancel: '取消'
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},
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refRelevance: {
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startDetect: '开始检测相关性',
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detecting: '检测中…',
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filterAll: '全部（{count}）',
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filterModify: '需修改（{count}）',
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columnTitle: 'AI 智能评定相关性建议',
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@@ -1,6 +1,7 @@
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<template>
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<div>
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<div class="crumbs">
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<div class="crumbs-header-bar">
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<div class="art_state_message_id" style="padding-left: 18px">
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<font
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>ID : <b style="margin-right: 25px">{{ form.accept_sn }}</b></font
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@@ -188,6 +189,17 @@
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</el-popover>
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</font>
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</div>
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<el-button
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v-if="showBeginProduceBtn"
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size="small"
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icon="el-icon-collection-tag"
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:loading="beginProduceLoading"
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class="crumbs-produce-btn"
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@click="beginProduce"
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>
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Begin Produce
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</el-button>
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</div>
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<div style="position: relative" v-if="currentArticleData.ai_review == ''">
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<el-tooltip
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class="item"
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@@ -1561,6 +1573,7 @@ export default {
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items: '',
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drawer: false,
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loading: false,
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beginProduceLoading: false,
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loading1: false,
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loading2: false,
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loading3: false,
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@@ -1656,6 +1669,7 @@ export default {
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manuscirpt: '',
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remarks: '',
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state: '',
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has_produce: null,
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ctime: '',
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is_draft: '',
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authorList: [],
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@@ -1910,6 +1924,11 @@ export default {
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}
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return frag;
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},
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showBeginProduceBtn() {
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const state = Number(this.form.state);
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const hasProduce = Number(this.form.has_produce);
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return state === 6 && hasProduce === 0;
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},
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articleState: function () {
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let str = '';
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@@ -1951,6 +1970,30 @@ export default {
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}
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},
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methods: {
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beginProduce() {
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const articleId = this.editform.articleId;
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if (!articleId) return;
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this.beginProduceLoading = true;
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this.$api
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.post('api/Production/addProduction', {
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article_id: articleId
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})
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.then((res) => {
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if (res.code == 0) {
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this.form.has_produce = 1;
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this.$message.success('Successfully added production instance!');
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} else {
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this.$message.error(res.msg || 'Failed to create production instance.');
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}
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})
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.catch((err) => {
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this.$message.error(typeof err === 'string' ? err : 'Failed to create production instance.');
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console.log(err);
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})
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.finally(() => {
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this.beginProduceLoading = false;
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});
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},
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reOpenDialog() {
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// 强制先关闭，确保状态发生位移
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this.copyrightDialogVisible = false;
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@@ -2670,6 +2713,7 @@ export default {
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this.repeform.repefen = res.article.repetition;
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this.remark.content = res.article.remarks;
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this.form.state = res.article.state;
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this.form.has_produce = res.article.has_produce;
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this.form.ctime = res.article.ctime;
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this.form.is_use_ai = res.article.is_use_ai;
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this.form.use_ai_explain = res.article.use_ai_explain;
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@@ -3196,6 +3240,40 @@ export default {
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</script>
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<style scoped>
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.crumbs-header-bar {
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display: flex;
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align-items: center;
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justify-content: space-between;
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gap: 16px;
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}
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.crumbs-produce-btn.el-button {
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flex-shrink: 0;
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margin-right: 18px;
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border: none;
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color: #fff;
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font-size: 13px;
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font-weight: 600;
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letter-spacing: 0.3px;
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padding: 10px 18px;
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border-radius: 4px;
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background: linear-gradient(135deg, #ffb347 0%, #ff7b00 48%, #e8590c 100%);
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box-shadow: 0 3px 10px rgba(232, 89, 12, 0.42);
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transition: transform 0.2s ease, box-shadow 0.2s ease, opacity 0.2s ease;
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}
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.crumbs-produce-btn.el-button:hover,
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.crumbs-produce-btn.el-button:focus {
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color: #fff;
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background: linear-gradient(135deg, #ffc46b 0%, #ff922b 48%, #f76707 100%);
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box-shadow: 0 5px 14px rgba(232, 89, 12, 0.5);
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transform: translateY(-1px);
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}
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.crumbs-produce-btn.el-button.is-loading {
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background: linear-gradient(135deg, #ffb347 0%, #ff7b00 48%, #e8590c 100%);
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}
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.art_state_ {
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padding: 20px 20px 1px 20px;
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}
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@@ -118,6 +118,16 @@
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>Delete</el-button
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>
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</div>
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<div v-if="role == 'editor'" class="ref-table-toolbar-right">
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<el-button
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type="primary"
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:loading="refRelevanceGlobalDetecting"
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:disabled="!chanFerForm.length"
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@click="startRefRelevanceDetect"
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>
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{{ $t('refRelevance.startDetect') }}
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</el-button>
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</div>
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<!-- <div v-if="showB_step == 2" class="ref-ai-filter-group">
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<el-radio-group v-model="refAiFilter" size="small" @change="onRefAiFilterChange">
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<el-radio-button label="all">{{ $t('refRelevance.filterAll', { count: refAiFilterStats.total }) }}</el-radio-button>
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@@ -200,7 +210,7 @@
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</template>
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</el-table-column>
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<!-- <el-table-column
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<el-table-column
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v-if="role == 'editor'"
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:label="$t('refRelevance.columnTitle')"
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width="340"
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@@ -208,7 +218,11 @@
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class-name="ref-relevance-column"
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>
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<template slot-scope="scope">
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<div v-if="isRefRelevanceUncited(scope.row)" class="ref-ai-cell ref-ai-cell--uncited">
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<div v-if="isRefRowRelevanceDetecting(scope.row)" class="ref-ai-cell ref-ai-cell--loading">
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<i class="el-icon-loading ref-ai-loading-icon"></i>
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<span>{{ $t('refRelevance.detecting') }}</span>
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</div>
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<div v-else-if="isRefRelevanceUncited(scope.row)" class="ref-ai-cell ref-ai-cell--uncited">
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<span class="ref-ai-tag ref-ai-tag--error">{{ $t('refRelevance.uncitedTag') }}</span>
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<p class="ref-ai-uncited-desc">{{ $t('refRelevance.uncitedDesc') }}</p>
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<p class="ref-ai-uncited-tip">{{ $t('refRelevance.uncitedTip') }}</p>
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@@ -232,9 +246,12 @@
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<div class="ref-ai-item-line">
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<div class="ref-ai-item-line-main">
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<span class="ref-ai-item-loc">{{ $t('refRelevance.citationN', { n: formatRelevanceCiteIndex(item, ri) }) }}</span>
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<span v-if="item.score != null" class="ref-ai-item-score">{{
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$t('refRelevance.relevancePctShort', { score: item.score })
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}}</span>
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<span
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v-if="item.score != null"
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class="ref-ai-item-score"
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:class="getRelevanceScoreClass(item)"
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>{{ $t('refRelevance.relevancePctShort', { score: item.score }) }}</span
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>
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<span
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class="ref-ai-item-verdict"
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:class="'verdict-' + getRelevanceCiteListTone(item)"
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@@ -247,7 +264,7 @@
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</div>
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<span v-else class="ref-ai-empty">—</span>
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</template>
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</el-table-column> -->
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</el-table-column>
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<el-table-column align="center" :width="role == 'editor' ? '330' : '290'">
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<div slot-scope="scope">
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@@ -647,9 +664,12 @@
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<span class="ref-ai-item-loc">{{
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$t('refRelevance.citationN', { n: formatRelevanceCiteIndex(citeItem, ri) })
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}}</span>
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<span v-if="citeItem.score != null" class="ref-ai-item-score">{{
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$t('refRelevance.relevancePctShort', { score: citeItem.score })
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}}</span>
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<span
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v-if="citeItem.score != null"
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class="ref-ai-item-score"
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:class="getRelevanceScoreClass(citeItem)"
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>{{ $t('refRelevance.relevancePctShort', { score: citeItem.score }) }}</span
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>
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<span
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class="ref-ai-item-verdict"
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:class="'verdict-' + getRelevanceCiteListTone(citeItem)"
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@@ -784,7 +804,9 @@ export default {
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relevanceActiveCiteMark: '',
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relevanceScrollTimer: null,
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/** 编辑端 AI 相关性列表筛选：all | modify */
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refAiFilter: 'all'
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refAiFilter: 'all',
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refRelevanceGlobalDetecting: false,
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refRelevanceDetectTimers: []
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};
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},
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computed: {
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@@ -866,6 +888,9 @@ export default {
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default: null
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}
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},
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beforeDestroy() {
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this.clearRefRelevanceDetectTimers();
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},
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watch: {
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article_id(val, oldVal) {
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if (val != null && val !== '' && val !== oldVal) {
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@@ -1010,29 +1035,126 @@ export default {
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return {};
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},
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init(e) {
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this.chanFerForm = this.attachMockRelevanceToRefs(e);
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this.clearRefRelevanceDetectTimers();
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this.refRelevanceGlobalDetecting = false;
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const rows = Array.isArray(e) ? e : [];
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if (this.role === 'editor') {
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rows.forEach((row) => this.resetRefRowRelevanceState(row));
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this.attachMockRelevanceToRefs(rows);
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this.applyMockRelevanceDisplayState(rows);
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this.$forceUpdate();
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}
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this.bijiao();
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////console.log('更新更新')
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},
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/** 临时 mock：编辑端引用相关性（后端接口就绪后删除） */
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/** 临时 mock：列表直接展示假数据（后端接口就绪后替换） */
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attachMockRelevanceToRefs(list) {
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const rows = Array.isArray(list) ? list : [];
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if (this.role !== 'editor') return rows;
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rows.forEach((row, index) => {
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if (!row || typeof row !== 'object') return;
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if (Array.isArray(row.ai_relevance_list) && row.ai_relevance_list.length) return;
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if (row.ai_relevance_uncited) return;
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const list = this.buildMockRelevanceList(index);
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if (list.length) {
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list.forEach((item) => this.enrichRelevanceAnnotations(item));
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row.ai_relevance_list = list;
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} else if (index === 3) {
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if (index === 4) {
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row.ai_relevance_uncited = true;
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row.ai_relevance_list = [];
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return;
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}
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const mockList = this.buildMockRelevanceList(index);
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if (mockList.length) {
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mockList.forEach((item) => this.enrichRelevanceAnnotations(item));
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row.ai_relevance_list = mockList;
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row.ai_relevance_uncited = false;
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}
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});
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return rows;
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},
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applyMockRelevanceDisplayState(rows) {
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(rows || []).forEach((row) => {
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if (!row) return;
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row._refAiState = 'done';
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const list = row.ai_relevance_list || [];
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row._refAiVisibleCount = list.length;
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});
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},
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resetRefRowRelevanceState(row) {
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if (!row || typeof row !== 'object') return row;
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delete row.ai_relevance_list;
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delete row.ai_relevance_uncited;
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row._refAiState = 'idle';
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row._refAiVisibleCount = 0;
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return row;
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},
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clearRefRelevanceDetectTimers() {
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(this.refRelevanceDetectTimers || []).forEach((id) => clearTimeout(id));
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this.refRelevanceDetectTimers = [];
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},
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isRefRowRelevanceDetecting(row) {
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return !!(row && row._refAiState === 'detecting');
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},
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startRefRelevanceDetect() {
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if (this.role !== 'editor' || this.refRelevanceGlobalDetecting) return;
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const rows = this.chanFerForm || [];
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if (!rows.length) return;
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this.clearRefRelevanceDetectTimers();
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this.refRelevanceGlobalDetecting = true;
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rows.forEach((row, index) => {
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if (!row) return;
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row._refAiState = 'detecting';
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row._refAiVisibleCount = 0;
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row.ai_relevance_list = [];
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row.ai_relevance_uncited = false;
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});
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this.$forceUpdate();
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const rowStagger = 480;
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const citeStagger = 320;
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rows.forEach((row, index) => {
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const t = setTimeout(() => {
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this.finishRefRowRelevanceDetect(row, index, citeStagger);
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if (index === rows.length - 1) {
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const tEnd = setTimeout(() => {
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this.refRelevanceGlobalDetecting = false;
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}, 400);
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this.refRelevanceDetectTimers.push(tEnd);
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}
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}, index * rowStagger + 520);
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this.refRelevanceDetectTimers.push(t);
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});
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},
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finishRefRowRelevanceDetect(row, index, citeStagger) {
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if (!row) return;
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if (index === 4) {
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row.ai_relevance_uncited = true;
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row.ai_relevance_list = [];
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row._refAiState = 'done';
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row._refAiVisibleCount = 0;
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this.$forceUpdate();
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return;
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}
|
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const list = this.buildMockRelevanceList(index);
|
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list.forEach((item) => this.enrichRelevanceAnnotations(item));
|
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row.ai_relevance_list = list;
|
||||
row.ai_relevance_uncited = false;
|
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row._refAiState = 'done';
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row._refAiVisibleCount = list.length;
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this.$forceUpdate();
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if (list.length && citeStagger > 0) {
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row._refAiVisibleCount = 0;
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this.revealRefRowCites(row, 0, citeStagger);
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}
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},
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revealRefRowCites(row, count, citeStagger) {
|
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const list = row.ai_relevance_list || [];
|
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if (count >= list.length) return;
|
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row._refAiVisibleCount = count + 1;
|
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this.$forceUpdate();
|
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const t = setTimeout(() => this.revealRefRowCites(row, count + 1, citeStagger), citeStagger);
|
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this.refRelevanceDetectTimers.push(t);
|
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},
|
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getRelevanceScoreClass(item) {
|
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const score = Number(item && item.score);
|
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if (isNaN(score)) return '';
|
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if (score > 85) return 'score-high';
|
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if (score < 20) return 'score-low';
|
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return 'score-mid';
|
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},
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enrichRelevanceAnnotations(item) {
|
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if (!item || (Array.isArray(item.annotations) && item.annotations.length)) return;
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const needFix = this.isRelevanceNeedModify(item);
|
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@@ -1102,101 +1224,13 @@ export default {
|
||||
section: 'Introduction',
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section_label: '引言',
|
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cite_snippet:
|
||||
'…the global cancer burden has increased substantially in recent decades [1], highlighting the need for updated estimates…',
|
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score: 98,
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'…deep learning has been widely applied to medical image analysis [1], yet comparative studies between CNN and ViT remain limited…',
|
||||
score: 91,
|
||||
level: 'high',
|
||||
label: '高相关',
|
||||
need_modify: false,
|
||||
modify_brief: '引用恰当',
|
||||
ai_desc: '此处用于支撑「全球癌症负担上升」的核心论点，与文献结论直接对应，引用恰当。'
|
||||
}
|
||||
],
|
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[
|
||||
{
|
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cite_index: 1,
|
||||
cite_total: 2,
|
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section: 'Methods',
|
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section_label: '方法',
|
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cite_snippet:
|
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'…incidence and mortality data were extracted from population-based cancer registries [2], following GLOBOCAN methodology…',
|
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score: 81,
|
||||
level: 'high',
|
||||
label: '较相关',
|
||||
need_modify: false,
|
||||
modify_brief: '可保留',
|
||||
ai_desc: '方法段引用：登记数据来源与统计框架一致，适合作为方法学依据。'
|
||||
},
|
||||
{
|
||||
cite_index: 2,
|
||||
cite_total: 2,
|
||||
section: 'Discussion',
|
||||
section_label: '讨论',
|
||||
cite_snippet:
|
||||
'…compared with previous reports [2], our estimates for lung cancer showed a divergent trend in females…',
|
||||
score: 58,
|
||||
level: 'medium',
|
||||
label: '部分相关',
|
||||
need_modify: true,
|
||||
modify_brief: '建议精简或改写',
|
||||
ai_desc: '讨论段引用：仅用于横向对比单一癌种趋势，与本文多癌种综合结果关联较弱，宜精简表述。'
|
||||
}
|
||||
],
|
||||
[
|
||||
{
|
||||
cite_index: 1,
|
||||
cite_total: 1,
|
||||
section: 'Introduction',
|
||||
section_label: '引言末段',
|
||||
cite_snippet: '…epidemiological transitions in low- and middle-income countries remain understudied [3]…',
|
||||
score: 72,
|
||||
level: 'medium',
|
||||
label: '背景相关',
|
||||
need_modify: true,
|
||||
modify_brief: '不宜作主要依据',
|
||||
ai_desc: '仅作区域背景铺垫，不宜作为全文主要结论来源。'
|
||||
}
|
||||
],
|
||||
[],
|
||||
[
|
||||
{
|
||||
cite_index: 1,
|
||||
cite_total: 3,
|
||||
section: 'Results',
|
||||
section_label: '结果',
|
||||
cite_snippet:
|
||||
'…the age-standardized incidence rate (ASIR) was 某值 per 100,000 [4], consistent with prior national reports…',
|
||||
score: 88,
|
||||
level: 'high',
|
||||
label: '高相关',
|
||||
need_modify: false,
|
||||
modify_brief: '可保留',
|
||||
ai_desc: '结果段第 1 处：ASIR 数据口径与文献一致，可直接对照。'
|
||||
},
|
||||
{
|
||||
cite_index: 2,
|
||||
cite_total: 3,
|
||||
section: 'Methods',
|
||||
section_label: '方法',
|
||||
cite_snippet: '…Bayesian hierarchical models were applied as described previously [4]…',
|
||||
score: 76,
|
||||
level: 'medium',
|
||||
label: '方法参考',
|
||||
need_modify: true,
|
||||
modify_brief: '需补充差异说明',
|
||||
ai_desc: '方法段第 2 处：借用建模思路，但研究人群与年份范围不同，需说明差异。'
|
||||
},
|
||||
{
|
||||
cite_index: 3,
|
||||
cite_total: 3,
|
||||
section: 'Discussion',
|
||||
section_label: '讨论',
|
||||
cite_snippet: '…future studies should integrate screening programs [4] to reduce mortality…',
|
||||
score: 42,
|
||||
level: 'low',
|
||||
label: '弱相关',
|
||||
need_modify: true,
|
||||
modify_brief: '建议删除',
|
||||
ai_desc: '讨论段第 3 处：文献侧重治疗与筛查路径，与本文流行病学估计主题偏离，建议弱化或删除。'
|
||||
ai_desc: '引言段用于概括深度学习在医学影像中的应用背景，与文献主题一致，引用恰当。'
|
||||
}
|
||||
],
|
||||
[
|
||||
@@ -1205,21 +1239,222 @@ export default {
|
||||
cite_total: 1,
|
||||
section: 'Introduction',
|
||||
section_label: '引言',
|
||||
cite_snippet: '…as reported in 2023 [6], whereas the reference publication year is 2024…',
|
||||
score: 65,
|
||||
cite_snippet:
|
||||
'…recent work compared CNN and Vision Transformers for COVID-19 detection on chest imaging [2], reporting competitive performance…',
|
||||
score: 96,
|
||||
level: 'high',
|
||||
label: '高相关',
|
||||
need_modify: false,
|
||||
modify_brief: '可保留',
|
||||
ai_desc: '直接支撑 CNN 与 ViT 在 COVID-19 影像检测中的对比论述，高度相关。'
|
||||
}
|
||||
],
|
||||
[
|
||||
{
|
||||
cite_index: 1,
|
||||
cite_total: 2,
|
||||
section: 'Methods',
|
||||
section_label: '方法',
|
||||
cite_snippet:
|
||||
'…we adopted a transfer-learning pipeline similar to prior brain MRI studies [3], including data augmentation and five-fold cross-validation…',
|
||||
score: 84,
|
||||
level: 'high',
|
||||
label: '较相关',
|
||||
need_modify: false,
|
||||
modify_brief: '可保留',
|
||||
ai_desc: '方法段引用：实验流程与文献中的 CNN/ViT 对比设置基本一致，可作为方法参考。'
|
||||
},
|
||||
{
|
||||
cite_index: 2,
|
||||
cite_total: 2,
|
||||
section: 'Discussion',
|
||||
section_label: '讨论',
|
||||
cite_snippet:
|
||||
'…unlike [3], our dataset focused on multi-class tumor grading rather than binary classification…',
|
||||
score: 55,
|
||||
level: 'medium',
|
||||
label: '部分相关',
|
||||
need_modify: true,
|
||||
modify_brief: '建议精简或改写',
|
||||
ai_desc: '讨论段仅用于对比任务设定差异，与本文核心结论关联偏弱，建议精简表述。'
|
||||
}
|
||||
],
|
||||
[
|
||||
{
|
||||
cite_index: 1,
|
||||
cite_total: 1,
|
||||
section: 'Introduction',
|
||||
section_label: '引言',
|
||||
cite_snippet:
|
||||
'…as reported in 2023 [4], whereas the cited work was published in 2024…',
|
||||
score: 63,
|
||||
level: 'medium',
|
||||
label: '待核实',
|
||||
need_modify: true,
|
||||
modify_brief: '年份不一致',
|
||||
ai_desc: '正文年份与文献出版年不一致，需核对后再保留该处引用。'
|
||||
}
|
||||
],
|
||||
[
|
||||
{
|
||||
cite_index: 1,
|
||||
cite_total: 3,
|
||||
section: 'Results',
|
||||
section_label: '结果',
|
||||
cite_snippet:
|
||||
'…ViT achieved higher accuracy than CNN on the brain MRI dataset [4], consistent with previous comparative analyses…',
|
||||
score: 93,
|
||||
level: 'high',
|
||||
label: '高相关',
|
||||
need_modify: false,
|
||||
modify_brief: '可保留',
|
||||
ai_desc: '结果段第 1 处：直接引用 CNN 与 ViT 在 Brain MRI 数据集上的性能对比结论，高度匹配。'
|
||||
},
|
||||
{
|
||||
cite_index: 2,
|
||||
cite_total: 3,
|
||||
section: 'Methods',
|
||||
section_label: '方法',
|
||||
cite_snippet: '…model training followed the protocol described in [4], with batch size 32 and Adam optimizer…',
|
||||
score: 78,
|
||||
level: 'medium',
|
||||
label: '方法参考',
|
||||
need_modify: true,
|
||||
modify_brief: '需补充差异说明',
|
||||
ai_desc: '方法段借用训练超参数设置，但数据集规模与预处理步骤不同，需补充说明。'
|
||||
},
|
||||
{
|
||||
cite_index: 3,
|
||||
cite_total: 3,
|
||||
section: 'Discussion',
|
||||
section_label: '讨论',
|
||||
cite_snippet: '…future work may explore lightweight ViT variants for edge deployment [4]…',
|
||||
score: 38,
|
||||
level: 'low',
|
||||
label: '弱相关',
|
||||
need_modify: true,
|
||||
modify_brief: '建议删除',
|
||||
ai_desc: '讨论段第 3 处：文献未涉及边缘部署主题，与本文实验结论关联较弱，建议弱化或删除。'
|
||||
}
|
||||
],
|
||||
[
|
||||
{
|
||||
cite_index: 1,
|
||||
cite_total: 1,
|
||||
section: 'Introduction',
|
||||
section_label: '引言',
|
||||
cite_snippet:
|
||||
'…hybrid CNN-Transformer architectures have also been proposed for radiological diagnosis [5]…',
|
||||
score: 68,
|
||||
level: 'medium',
|
||||
label: '背景相关',
|
||||
need_modify: true,
|
||||
modify_brief: '不宜作主要依据',
|
||||
ai_desc: '仅作混合架构背景铺垫，不宜作为本文 CNN vs ViT 对比的主要论据。'
|
||||
}
|
||||
],
|
||||
[
|
||||
{
|
||||
cite_index: 1,
|
||||
cite_total: 1,
|
||||
section: 'Results',
|
||||
section_label: '结果',
|
||||
cite_snippet:
|
||||
'…as shown in [6], ViT outperformed CNN when sufficient training data were available…',
|
||||
score: 90,
|
||||
level: 'high',
|
||||
label: '高相关',
|
||||
need_modify: false,
|
||||
modify_brief: '可保留',
|
||||
ai_desc: '与重复条目 [4] 相同文献，此处引用结论与正文数据量依赖性论述一致。'
|
||||
}
|
||||
],
|
||||
[
|
||||
{
|
||||
cite_index: 1,
|
||||
cite_total: 1,
|
||||
section: 'Discussion',
|
||||
section_label: '讨论',
|
||||
cite_snippet:
|
||||
'…attention mechanisms in ViT may improve interpretability over conventional CNNs [7]…',
|
||||
score: 82,
|
||||
level: 'high',
|
||||
label: '较相关',
|
||||
need_modify: false,
|
||||
modify_brief: '可保留',
|
||||
ai_desc: '讨论段引用 ViT 可解释性优势，与本文模型对比讨论方向一致。'
|
||||
}
|
||||
],
|
||||
[
|
||||
{
|
||||
cite_index: 1,
|
||||
cite_total: 2,
|
||||
section: 'Methods',
|
||||
section_label: '方法',
|
||||
cite_snippet: '…preprocessing included skull stripping and intensity normalization as in [8]…',
|
||||
score: 74,
|
||||
level: 'medium',
|
||||
label: '部分相关',
|
||||
need_modify: true,
|
||||
modify_brief: '建议精简或改写',
|
||||
ai_desc: '预处理流程引用合理，但文献针对 CT 影像，与本文 MRI 模态不完全匹配，宜注明差异。'
|
||||
},
|
||||
{
|
||||
cite_index: 2,
|
||||
cite_total: 2,
|
||||
section: 'Introduction',
|
||||
section_label: '引言',
|
||||
cite_snippet: '…automated screening systems have reduced diagnostic workload [8]…',
|
||||
score: 48,
|
||||
level: 'low',
|
||||
label: '弱相关',
|
||||
need_modify: true,
|
||||
modify_brief: '建议删除',
|
||||
ai_desc: '筛查系统论述与本文模型性能对比主题偏离，建议删除或改述。'
|
||||
}
|
||||
],
|
||||
[
|
||||
{
|
||||
cite_index: 1,
|
||||
cite_total: 1,
|
||||
section: 'Discussion',
|
||||
section_label: '讨论',
|
||||
cite_snippet:
|
||||
'…ensemble methods combining CNN and ViT predictions may further improve robustness [9]…',
|
||||
score: 87,
|
||||
level: 'high',
|
||||
label: '高相关',
|
||||
need_modify: false,
|
||||
modify_brief: '引用恰当',
|
||||
ai_desc: '讨论段展望集成策略，与全文 CNN/ViT 对比研究脉络一致，可保留。'
|
||||
}
|
||||
],
|
||||
[
|
||||
{
|
||||
cite_index: 1,
|
||||
cite_total: 1,
|
||||
section: 'Conclusion',
|
||||
section_label: '结论',
|
||||
cite_snippet:
|
||||
'…our findings align with prior surveys on transformer-based medical imaging [10]…',
|
||||
score: 94,
|
||||
level: 'high',
|
||||
label: '高相关',
|
||||
need_modify: false,
|
||||
modify_brief: '可保留',
|
||||
ai_desc: '结论段综述性引用，用于归纳 transformer 在医学影像中的研究趋势，匹配度高。'
|
||||
}
|
||||
]
|
||||
];
|
||||
return pools[index % pools.length] || [];
|
||||
return pools[index] || pools[index % pools.length] || [];
|
||||
},
|
||||
getRefRelevanceList(row) {
|
||||
if (!row || !Array.isArray(row.ai_relevance_list)) return [];
|
||||
const list = row.ai_relevance_list.filter((item) => item && (item.ai_desc || item.score != null));
|
||||
let list = row.ai_relevance_list.filter((item) => item && (item.ai_desc || item.score != null));
|
||||
const visible = row._refAiVisibleCount;
|
||||
if (row._refAiState === 'done' && visible != null && visible > 0 && visible < list.length) {
|
||||
list = list.slice(0, visible);
|
||||
}
|
||||
return list.slice().sort((a, b) => {
|
||||
const ia = Number(a.cite_index);
|
||||
const ib = Number(b.cite_index);
|
||||
@@ -3449,6 +3684,23 @@ export default {
|
||||
flex-wrap: wrap;
|
||||
gap: 0;
|
||||
}
|
||||
.ref-table-toolbar-right {
|
||||
flex-shrink: 0;
|
||||
margin-left: auto;
|
||||
}
|
||||
.ref-ai-cell--loading {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
gap: 8px;
|
||||
min-height: 48px;
|
||||
color: #606266;
|
||||
font-size: 12px;
|
||||
}
|
||||
.ref-ai-loading-icon {
|
||||
font-size: 16px;
|
||||
color: #409eff;
|
||||
}
|
||||
.ref-ai-filter-group {
|
||||
flex-shrink: 0;
|
||||
}
|
||||
@@ -3694,6 +3946,18 @@ export default {
|
||||
color: #606266;
|
||||
flex-shrink: 0;
|
||||
}
|
||||
.ref-ai-item-score.score-high {
|
||||
color: #389e0d;
|
||||
font-weight: 600;
|
||||
}
|
||||
.ref-ai-item-score.score-mid {
|
||||
color: #d48806;
|
||||
font-weight: 600;
|
||||
}
|
||||
.ref-ai-item-score.score-low {
|
||||
color: #cf1322;
|
||||
font-weight: 600;
|
||||
}
|
||||
.ref-ai-item-verdict {
|
||||
margin: 0;
|
||||
font-weight: 600;
|
||||
|
||||
Reference in New Issue
Block a user