Biological Impacts of Thermal Extremes: Mechanisms and Costs of Functional Responses Matter
Bibliographic record
Abstract
Thermal performance curves enable physiological constraints to be incorporated in predictions of biological responses to shifts in mean temperature. But do thermal performance curves adequately capture the biological impacts of thermal extremes? Organisms incur physiological damage during exposure to extremes, and also mount active compensatory responses leading to acclimatization, both of which alter thermal performance curves and determine the impact that current and future extremes have on organismal performance and fitness. Thus, these sub-lethal responses to extreme temperatures potentially shape evolution of thermal performance curves. We applied a quantitative genetic model and found that beneficial acclimatization and cumulative damage alter the extent to which thermal performance curves evolve in response to thermal extremes. The impacts of extremes on the evolution of thermal performance curves are reduced if extremes cause substantial mortality or otherwise reduce fitness differences among individuals. Further empirical research will be required to understand how responses to extremes aggregate through time and vary across life stages and processes. Such research will enable incorporating passive and active responses to sub-lethal stress when predicting the impacts of thermal extremes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".