用四重机制构建可审计的物种性状提取系统，实现大规模精准记录。
A Registry-Bound LLM Pipeline for Evidence-Grounded Trait Extraction across Tropical Plants, Aquatic Species, and Exotic Pets
- 基于版本化性状注册表约束输出，确保结构规范
- 每条记录附原文引用与置信度标签，99.985%物种成功提取
- 三重验证保障证据可信，适合生物数据标准化需求
我们提出一种基于注册表的大型语言模型提取流水线，可规模化生成栽培热带植物、水生生物和异宠物种的证据支撑型结构化性状记录。四个机制保障输出可审计：版本化的39个键值闭合词汇性状注册表，将每个取值限定于类型化模式；每条记录附原文引文，关联原始文本；每条记录标注置信度（高或中；低者不持久化）；多版本保存。在《热带物种百科全书》409,880个可发表物种上执行706,220次运行，共持久化5,489,881条性状记录，覆盖409,820个物种（99.985%），其中81.57%为高置信度。报告三重验证层：全量样本中，5,427,588条带证据行中有90.12%的引文是源文本的完整子串（剔除一个合规元性状后为93.49%）；对100条分层非红区样本的引文-值一致性审计得100/100（下限96.30%）；对50条红区样本的外观有效性评估得50/50接受（下限92.86%）。单条记录正确性未声称，仍待人工校验。贡献在于四机制框架。
原文摘要 · Abstract (English)
We describe a registry-bound large-language-model extraction pipeline producing evidence-grounded structured trait records at scale, on cultivated tropical plant, aquatic, and pet species. Four mechanisms render LLM-derived rows auditable: a versioned 39-key closed-vocabulary trait registry constraining every admitted value to a typed schema; a per-row verbatim evidence quote tying each value to source text; a per-row confidence label (high or medium; low dropped pre-persist); and multi-version preservation. Applied to 409,880 publishable species from the Tropical Species Encyclopedia, the pipeline executed 706,220 runs and persisted 5,489,881 trait records across 409,820 species (99.985%), 81.57% at high confidence. We report three validation layers in descending evidentiary strength: at full population, 90.12% of 5,427,588 evidence-bearing rows have their quote as a verbatim source substring (93.49% excluding one compliance meta-trait); a quote-supports-value audit on n=100 stratified non-red-zone rows yielded 100/100 (lower bound 96.30%); face-validity on n=50 red-zone rows yielded 50/50 Accept (lower bound 92.86%). Per-record correctness is not claimed; 100% pending human curation. The contribution is the four-mechanism framework.
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