{
  "schemaVersion": "1.0.0",
  "datasetId": "ai-search-rag-baidu-serp-entry-liveness-2026-07-31-185000",
  "capturedAt": "2026-07-31T18:57:51Z",
  "observationMode": "anonymous_read_only_live_fetch",
  "readOnly": true,
  "targetUrl": "https://course.dragonai.tech/research/ai-search-rag-finetuning-citations-2026-07",
  "releaseId": "20260731T130258Z-c8abe8ac79d6",
  "previousSearchObservedAt": "2026-07-31T18:33:38Z",
  "currentSearchObservedAt": "2026-07-31T18:50:00Z",
  "publicJsonUrl": "https://course.dragonai.tech/datasets/ai-search-rag-baidu-serp-entry-liveness-2026-07-31-185000.json",
  "publicCsvUrl": "https://course.dragonai.tech/datasets/ai-search-rag-baidu-serp-entry-liveness-2026-07-31-185000.csv",
  "sourceBindings": [
    {
      "role": "previous_strict_baidu_visibility",
      "path": "geo/reports/2026-07-31T183338Z-ai-search-rag-citation-benchmark-baidu-search-visibility.json",
      "sha256": "361da4f39f5a66109c5f0a7eca26cf7f3a6cb7957cc929fa88e0fbdb5e8e2273"
    },
    {
      "role": "current_strict_baidu_visibility",
      "path": "geo/reports/2026-07-31T185000Z-ai-search-rag-citation-benchmark-baidu-search-visibility.json",
      "sha256": "559f30d2195d0f320e0fa5caa3dbecf4610fe11dfdad378e6086d0a3a0750b04"
    }
  ],
  "summary": {
    "enteredUrlCount": 1,
    "exitedUrlCount": 1,
    "enteredLiveHtmlCount": 0,
    "enteredHttpErrorCount": 1,
    "targetObservedInCurrentStrictQueries": false,
    "providerCallsMade": false,
    "countsAsSearchVisibility": false,
    "countsAsSourceQuality": false,
    "countsAsGeoSuccess": false
  },
  "enteredUrlObservations": [
    {
      "queryId": "rag-citation-selection-sources",
      "query": "RAG 和微调选型 AI 引用哪些来源",
      "position": 7,
      "title": "RAG 还是微调?手把手教你根据需求选对 AI 模型优化方案 - 知乎",
      "url": "https://zhuanlan.zhihu.com/p/2024919544519500917",
      "fetchStatus": "http_error",
      "httpStatus": 403,
      "finalUrl": "https://zhuanlan.zhihu.com/p/2024919544519500917",
      "redirected": null,
      "contentType": "text/html",
      "responseBytes": null,
      "responseSha256": null,
      "pageTitle": null,
      "pageTitleLength": null,
      "metaDescriptionPresent": null,
      "metaDescriptionLength": null,
      "canonicalUrl": null,
      "canonicalSameAsFinalUrl": null,
      "h1Count": null,
      "h2Count": null,
      "h3Count": null,
      "tableCount": null,
      "preOrCodeCount": null,
      "figureCount": null,
      "imageCount": null,
      "jsonldBlockCount": null,
      "jsonldTypes": [],
      "sameHostLinkCount": null,
      "externalLinkCount": null,
      "downloadableLinkCount": null,
      "downloadableLinkUrls": [],
      "countsAsSearchVisibility": false,
      "countsAsSourceQuality": false,
      "countsAsGeoSuccess": false
    }
  ],
  "exitedUrls": [
    {
      "queryId": "rag-citation-selection-sources",
      "query": "RAG 和微调选型 AI 引用哪些来源",
      "position": 8,
      "title": "RAG vs 微调:哪种方式更适合企业级AI应用?",
      "url": "https://baijiahao.baidu.com/s?id=1851203837024012765&wfr=spider&for=pc"
    }
  ],
  "evidenceBoundaries": [
    "The source ranking reports remain the authority for observed Baidu positions; this live fetch does not rewrite or invalidate them.",
    "An HTTP error after a URL entered the result set shows a liveness mismatch at capture time, not a ranking cause or source-quality judgment.",
    "The target remained absent from every strict unbranded query in the current source report.",
    "No model provider was called and no external content, account, submission or indexing endpoint was mutated."
  ],
  "integrity": {
    "algorithm": "sha256",
    "canonicalCoreSha256": "072cee201b37d48da064ef42fea79cb9542177884af7c319d70647a33b31a265"
  },
  "title": "百度严格无品牌结果新进入 URL 存活性复查（2026-07-31 18:50 UTC）",
  "description": "对后续相邻严格百度观测中新进入结果集的 URL 再做匿名只读抓取，保留 HTTP 存活性变化；该复查不改写排名，也不构成 DragonAI 搜索可见性或 GEO 成功。",
  "url": "https://course.dragonai.tech/datasets/ai-search-rag-baidu-serp-entry-liveness-2026-07-31-185000.json",
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "creator": "DragonAI 内容研究组"
}