AmphiTherm: a comprehensive database of amphibian thermal tolerance and preference
Bibliographic record
Abstract
Thermal traits are crucial to our understanding of the ecology and physiology of ectothermic animals. While rising global temperatures have increasingly pushed research towards the study of upper thermal limits, lower thermal limits and thermal preferences are essential for defining the thermal niche of ectotherms. Through a systematic review of the literature in seven languages, we expanded an existing database of amphibian heat tolerance by adding 1,009 estimates of cold tolerance and 816 estimates of thermal preference across 375 species. AmphiTherm is a comprehensive and reproducible database that contains 4,899 thermal trait estimates from a diverse sample of 659 species (~7.5% of all described amphibians) spanning 38 families. Despite its broad geographic coverage, we report evident gaps across amphibian biodiversity hotspots in Africa, most regions of Asia, central South America, and Western Australia. By providing a more holistic understanding of amphibian thermal tolerance and preferences, AmphiTherm is a valuable resource for advancing research in evolutionary biology, ecophysiology, and biogeography of amphibians, offering insights that are increasingly needed in changing climates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".