Biological Impacts of Thermal Extremes: Mechanisms and Costs of Functional Responses Matter
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it