A comprehensive database of amphibian heat tolerance
Bibliographic record
Abstract
Rising temperatures represent a significant threat to the survival of ectothermic animals. As such, upper thermal limits represent an important trait to assess the vulnerability of ectotherms to changing temperatures. For instance, one may use upper thermal limits to estimate current and future thermal safety margins (i.e., the proximity of upper thermal limits to experienced temperatures), use this trait together with other physiological traits in species distribution models, or investigate the plasticity and evolvability of these limits for buffering the impacts of changing temperatures. While datasets on thermal tolerance limits have been previously compiled, they sometimes report single estimates for a given species, do not present measures of data dispersion, and are biased towards certain parts of the globe. To overcome these limitations, we systematically searched the literature in seven languages to produce the most comprehensive dataset to date on amphibian upper thermal limits, spanning 3,095 estimates across 616 species. This resource will represent a useful tool to evaluate the vulnerability of amphibians, and ectotherms more generally, to changing temperatures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.173 | 0.001 |
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; both teacher heads agree on what is shown here.
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".