Interpreting empirical estimates of experimentally derived physiological and biological thermal limits in ectotherms
Bibliographic record
Abstract
Whole-organism function is underpinned by physiological and biological processes, which respond to temperature over a range of time scales. Given that environmental temperature controls biological rates within ectotherms, different experimental protocols are needed to assess the ability of organisms to withstand extreme weather events versus gradual temperature change. Here we emphasize the importance of time in shaping ecological and evolutionary processes, and as an experimental parameter that is key when interpreting physiology studies reporting thermal limits. We discuss how acute and chronic thermal performance is underpinned by mechanisms operating at different time scales — resistance, acclimation, and adaptation. We offer definitions of common physiological and biological temperature metrics and identify challenges inherent to compiling the wealth of historical temperature limit data now available into meta-analytic frameworks. We use a case study, data across temperate fishes, to highlight that false positives may occur when differences in the thermal tolerances of species are in fact due to experimental protocols. We further illustrate that false negatives can arise if researchers fail to recognize differences in thermal limits of species emerging from macrophysiological approaches that are due to biological mechanisms. We strongly advocate for the careful design, interpretation, and reporting of experimental results to ensure that conclusions arising from data synthesis efforts are grounded in theory.
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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 | 0.099 | 0.329 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".