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      "ylabel": "Expected improvement",
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  ],
  "title": "Measurement reliability and acquisition choice shape discovery",
  "assumptions": "Sensitivity 0.9, hypothetical prevalence/specificity. Gaussian predictive EI for maximization over best=0. No experiment or material data.",
  "worked": "目标比例0.1时，特异度0.9得到PPV=0.5，特异度0.99时约0.909091。均值0时高斯EI=sigma/sqrt(2pi)。",
  "checks": {
    "ppv_09": 0.5,
    "ppv_099": 0.9090909090909092,
    "ei_mu0_sigma1": 0.3989422804014327
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  "generator_source_sha256": {
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    "ops/datacenter/editorial_guides/deep_figures.py": "158969d9b1f5a4bd13906cf78dd71affc2692df9194593918059d861a6f59c85"
  },
  "source_ids": [
    "alab",
    "coscientist",
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  ],
  "formula_source": "Article equations and this versioned script",
  "diagram_nodes": [
    "冻结目标和预算",
    "候选与主动决策",
    "幂等任务执行",
    "原始测量及替代解释",
    "独立确认与复核",
    "全分母知识账本"
  ]
}