A Neuro-Symbolic Bioinformatics Framework for Unlocking Chordate Physiological Dark Data and Validating Allometric Scaling

Animal functional trait data are essential for macroecology, but massive datasets remain locked in unstructured scientific literature. Traditional manual extraction is inefficient, and general-purpose artificial intelligence (AI) systems struggle with complex biological tables and numerical accuracy. To address this bioinformatics challenge, we propose a multimodal neuro-symbolic framework combining visual-language perception and code-based reasoning. This approach reconstructs complex document layouts and delegates biostatistical calculations, such as unit normalization and thermodynamic energy conversion, to an isolated programming environment to ensure mathematical and statistical consistency. By mining literature spanning 117 years, we constructed a high-fidelity physiological database for 1632 chordate species. Our method achieved a macro-averaged F1 score of 0.935 in extracting biophysical fields. External benchmarking against a curated mammalian trait database showed strong concordance for shared body-mass and metabolic-rate traits, while our database retained record-level provenance and physiological context. Furthermore, the extracted data reproduced classic allometric scaling relationships for basal metabolic rate and brain volume while preserving physiological adaptations, supporting the biological plausibility of the dataset. This study validates a reproducible bioinformatics pipeline that minimizes extraction artifacts and substantially reduces downstream mathematical and statistical conversion errors, while providing a scalable, complementary resource for building physiology-oriented trait databases from historical literature.