Linking critical thermal maximum to mortality from thermal stress in a cold-water frog
Bibliographic record
Abstract
Estimates of organismal thermal tolerance are frequently used to assess physiological risk from warming, yet the assumption that these estimates are predictive of mortality has been called into question. We tested this assumption in the cold-water-specialist frog, Ascaphus montanus . For seven populations, we used dynamic experimental assays to measure tadpole critical thermal maximum (CTmax) and measured mortality from chronic thermal stress for 3 days at different temperatures. We tested the relationship between previously estimated population CTmax and observed mortality, as well as the strength of CTmax as a predictor of mortality compared to local stream temperatures capturing varying timescales. Populations with higher CTmax experienced significantly less mortality in the warmest temperature treatment (25°C). We also found that population CTmax outperformed stream temperature metrics as the top predictor of observed mortality. These results demonstrate a clear link between CTmax and mortality from thermal stress, contributing evidence that CTmax is a relevant metric for physiological vulnerability assessments.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".