Global dataset for realized thermal and aridity niche limits for terrestrial vertebrates
Bibliographic record
Abstract
Climate changes are altering temperature and aridity regimes, posing serious challenges for many species globally. Particularly, environmental changes that create conditions that push species beyond there realized thermal and aridity niche limits are especially likely to contribute to extinction risk. Yet, responses to climate change vary interspecifically for geographical range shifts, the timing of biological activities, trends in body size, and population growth differences. Estimates of thermal and aridity niche limits help predict and understand such variation. For such studies that address how species respond to climate change over broad geographical areas, we created a dataset of realized niche limits for 33,941 species of terrestrial vertebrates for (amphibians, birds, mammals, reptiles). Specifically, these datasets include monthly lower and upper realized thermal and aridity niche limits, along with yearly average estimates of niche limits. These datasets will be curated to expand taxonomic coverage of realized niche limits and facilitate evaluation of impacts of climate change on biodiversity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.011 |
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".