{
  "centralThesis": "围绕“AI for Science：领域全景与核心共识：关于 AI for Science，现有研究形成了哪些较可信且容易理解的核心结论？主要有哪些研究分支和代表性证据？”，应先区分当前证据直接支持的结论与仍待验证的推断。",
  "evidenceMode": "fulltext",
  "modules": [
    {
      "argumentRole": "orient",
      "avoidRepeatingClaimIds": [],
      "claimIds": [],
      "confidencePolicy": "evidence_calibrated",
      "confusionToResolve": "",
      "evidencePaperIds": [
        "openalex:W4401726605",
        "openalex:W4412642369",
        "openalex:W4406828533"
      ],
      "exampleRequirement": "none",
      "id": "M1",
      "includeReason": "先建立读者理解后续结论所需的共同语境。",
      "kind": "orientation",
      "lengthBudget": 300,
      "readerQuestion": "这项研究问题的范围和阅读入口是什么？",
      "readerTakeaway": "先明确问题范围和阅读入口。",
      "renderMode": "prose",
      "requirements": [
        "说明问题边界",
        "避免把研究背景写成结论"
      ],
      "title": "如何理解这个问题",
      "transitionFromPrevious": "开篇建立共同语境。"
    },
    {
      "argumentRole": "answer",
      "avoidRepeatingClaimIds": [],
      "claimIds": [
        "C1",
        "C2",
        "C3",
        "C4",
        "C5",
        "C6",
        "C7",
        "C8"
      ],
      "confidencePolicy": "evidence_calibrated",
      "confusionToResolve": "",
      "evidencePaperIds": [
        "openalex:W4401726605",
        "openalex:W4412642369",
        "openalex:W4406828533",
        "arxiv:2604.27297",
        "arxiv:2508.14111",
        "arxiv:2509.02661",
        "openalex:W3205208140",
        "openalex:W4323697696",
        "arxiv:2607.09025",
        "arxiv:2310.18852",
        "arxiv:2406.10557",
        "arxiv:2401.11839",
        "arxiv:2501.11847",
        "arxiv:2608.02775",
        "openalex:W4402901051",
        "arxiv:2110.01831",
        "arxiv:2505.03977",
        "arxiv:2603.28361",
        "arxiv:2104.08043"
      ],
      "exampleRequirement": "concrete_example",
      "id": "M2",
      "includeReason": "让读者先获得能够独立理解的结论。",
      "kind": "core_conclusions",
      "lengthBudget": 1100,
      "readerQuestion": "当前证据最直接支持哪些结论？",
      "readerTakeaway": "读者能够复述当前证据支持的核心认识。",
      "renderMode": "prose",
      "requirements": [
        "每条结论说明重要性",
        "结论与证据强度相匹配"
      ],
      "title": "目前可以带走的核心结论",
      "transitionFromPrevious": "在问题定向后直接回答研究问题。"
    },
    {
      "argumentRole": "synthesize",
      "avoidRepeatingClaimIds": [],
      "claimIds": [
        "C1",
        "C2",
        "C3",
        "C4",
        "C5",
        "C6",
        "C7",
        "C8",
        "C9",
        "C10",
        "C11",
        "C12"
      ],
      "confidencePolicy": "evidence_calibrated",
      "confusionToResolve": "",
      "evidencePaperIds": [
        "openalex:W4401726605",
        "openalex:W4412642369",
        "openalex:W4406828533",
        "arxiv:2604.27297",
        "arxiv:2508.14111",
        "arxiv:2509.02661",
        "openalex:W3205208140",
        "openalex:W4323697696",
        "arxiv:2607.09025",
        "arxiv:2310.18852",
        "arxiv:2406.10557",
        "arxiv:2401.11839",
        "arxiv:2501.11847",
        "arxiv:2608.02775",
        "openalex:W4402901051",
        "arxiv:2110.01831",
        "arxiv:2505.03977",
        "arxiv:2603.28361",
        "arxiv:2104.08043"
      ],
      "exampleRequirement": "contrast_pair",
      "id": "M3",
      "includeReason": "帮助读者理解不同工作之间的关系。",
      "kind": "research_landscape",
      "lengthBudget": 900,
      "readerQuestion": "现有研究主要从哪些问题入口展开？",
      "readerTakeaway": "读者能够理解不同工作围绕哪些问题形成分支。",
      "renderMode": "map",
      "requirements": [
        "按问题而不是论文顺序组织",
        "说明各分支之间的关系"
      ],
      "title": "当前研究版图",
      "transitionFromPrevious": "核心结论之后解释这些认识在研究版图中的关系。"
    },
    {
      "argumentRole": "assess_evidence",
      "avoidRepeatingClaimIds": [],
      "claimIds": [
        "C1",
        "C2",
        "C3",
        "C4",
        "C5",
        "C6",
        "C7",
        "C8",
        "C9",
        "C10",
        "C11",
        "C12"
      ],
      "confidencePolicy": "evidence_calibrated",
      "confusionToResolve": "",
      "evidencePaperIds": [
        "openalex:W4401726605",
        "openalex:W4412642369",
        "openalex:W4406828533",
        "arxiv:2604.27297",
        "arxiv:2508.14111",
        "arxiv:2509.02661",
        "openalex:W3205208140",
        "openalex:W4323697696",
        "arxiv:2607.09025",
        "arxiv:2310.18852",
        "arxiv:2406.10557",
        "arxiv:2401.11839",
        "arxiv:2501.11847",
        "arxiv:2608.02775",
        "openalex:W4402901051",
        "arxiv:2110.01831",
        "arxiv:2505.03977",
        "arxiv:2603.28361",
        "arxiv:2104.08043"
      ],
      "exampleRequirement": "none",
      "id": "M4",
      "includeReason": "防止把有限证据写成领域共识。",
      "kind": "evidence_boundaries",
      "lengthBudget": 700,
      "readerQuestion": "当前证据没有回答什么？",
      "readerTakeaway": "读者能够区分已获支持的判断与仍待验证的推断。",
      "renderMode": "prose",
      "requirements": [
        "区分缺失信息和反对证据",
        "明确仍需全文或新研究验证的部分"
      ],
      "title": "这些结论能相信到什么程度",
      "transitionFromPrevious": "在全文结尾校准前述判断的适用范围。"
    }
  ],
  "narrativeArc": [
    "先明确问题范围和阅读入口。",
    "读者能够复述当前证据支持的核心认识。",
    "读者能够理解不同工作围绕哪些问题形成分支。",
    "读者能够区分已获支持的判断与仍待验证的推断。"
  ],
  "omittedModules": [
    {
      "kind": "method_evolution",
      "reason": "未在规划阶段确认足够清晰的问题—方法—代价演进链。"
    },
    {
      "kind": "system_layers",
      "reason": "未在规划阶段确认稳定的系统层级关系。"
    }
  ],
  "readerTakeaways": [
    "AI for Science 的核心共识是，其成功不仅依赖算法改进，还取决于跨学科社区建设、数据基础设施和开放科学机制；单纯方法突破不足以解决科学采用障碍。",
    "在材料科学和生物化学等领域，高质量、大规模且标准化的数据是 AI 模型性能的主要限制因素；仅遵循 FAIR 原则不足以保证 AI 就绪性，还需要评估数据充分性。",
    "多个分支强调可解释性和人类专家介入对科学发现至关重要；仅靠事后解释不足以保证科学结论，需要结合准确性、可再现性和可理解性标准，并用实验进行裁决。",
    "AI for Science 正在从单一预测模型向自主发现、闭环实验的‘代理科学’演进，但当前成功案例（如虚拟实验室、机器集体智能）主要是在受控基准或数字阶段的存在性证明，尚未形成可推广到物理实验的成熟能力。",
    "物理信息神经网络（PINNs）通过将偏微分方程嵌入损失函数，在流体、固体力学等领域展现出作为数值求解器替代或补充的潜力，但在高维问题、可扩展性和可靠性方面仍存在显著未解决挑战。",
    "生物与化学科学大语言模型已发展出文本、分子、蛋白质、基因组和多模态五类，但评估基础设施滞后：缺乏大学后水平基准，且计算指标不能直接代表湿实验有效性。",
    "AlphaFold2 的高置信度预测并不等于生物学上正确的结构；部分预测（如 CENP-E、Mad2）与实验结构不符，说明实验结构生物学仍是判定生物学真实状态和构象的必要手段。",
    "因果发现基准实验表明，时间序列因果推断方法（如 Granger、PCMCI、DYNOTEARS）的性能对因果充分性、线性、无瞬时效应等假设高度敏感；违反假设时 F1 显著下降，且超参数选择（如 PCMCI 的 p 值阈值）对结果影响强烈。"
  ],
  "schemaVersion": 2
}