StoichLife: A global dataset of plant and animal elemental content
Bibliographic record
Abstract
The elemental composition of life is a fundamental trait that shapes ecology and evolution. Yet, organismal stoichiometry has only been extensively studied on a case-by-case basis, and it remains unclear whether observed patterns and underlying mechanisms are generalizable across major taxa and realms. To address this gap, we introduce "StoichLife", a comprehensive database encompassing 28,049 individual records spanning 5,876 species or morphospecies across 227 datasets. Derived from published and unpublished sources, StoichLife documents elemental concentrations and stoichiometry (i.e., %C, %N, %P, C:N, C:P, and N:P) for individual-level plants and animals from terrestrial, freshwater, and marine realms. The standardized records are accompanied by information, if available, on taxonomy, habitat, body mass (for animals), geographic location, and environmental conditions (e.g., aquatic vs. terrestrial, temperature, solar radiation) of sampling sites. The StoichLife database offers an unparalleled opportunity to unravel both overarching patterns and context-dependence in global ecological stoichiometry across taxa and realms, providing insight into the chemical composition of life and its responses to environmental change.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.019 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".