发表机构
Lossfunk（洛斯芬克）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究探讨元数据缺口对学术人工智能归因的影响，通过边界测试和真实学术基础设施测试发现，缺失链接会致归因错误，只有记录连接可见或可恢复时人工智能系统才认可工作，进而提出关联分数用于检查元数据缺口以指导修复。
AI 中文摘要
人工智能系统在科学发现与认可过程中作用日益重要。研究探讨缺失元数据是否会阻碍人工智能系统对研究工作的认可。通过边界测试发现，无任务相关论文列表时，人工智能系统引用会出现不在列表中的标识符，甚至有伪造的。在真实学术基础设施中测试机制，利用OpenAlex记录隐藏或恢复作者、机构、资助者、参考文献及文本访问链接等。恢复相关链接才能实现相应归因，恢复错误链接则不行，469次不匹配测试均无正确答案。缺失链接会导致错误答案、拒绝或工具预算耗尽，网络搜索无法恢复隐藏作者链接。总之，只有记录连接可见或可恢复时，人工智能系统才认可工作。这促使提出用于检查元数据缺口的记录级关联分数，以指导修复并为人工智能介导的学术记录使用做准备。
英文摘要
Artificial intelligence systems increasingly mediate how science is found and credited. We asked whether missing metadata prevents AI systems from crediting work. As a boundary test, an AI system citing without access to task-relevant paper lists often produced out-of-list identifiers, some fabricated. We then tested the mechanism in real scholarly infrastructure by using OpenAlex records to hide or restore author, institution, funder, reference, and text-access links while holding works and tasks fixed. Restoring the relevant link made the corresponding attribution possible; restoring the wrong kind did not, with 0 correct answers across 469 completed mismatched tests. Thus, in these tasks, one metadata facet did not substitute for another. Missing links led to invented answers, refusals, or tool-budget exhaustion, and web search did not recover hidden author links. In sum, AI systems credited work only when record connections were visible or recoverable. This motivates Nexus-Score, a record-level check for metadata gaps, to guide repair and help prepare the scholarly record for AI-mediated use.
Comments19 pages, 4 figures, 10 tables; includes supplementary methods and reproducibility information
