The importance of incorporating natural thermal variation when evaluating physiological performance in wild species
Bibliographic record
Abstract
Environmental variability in aquatic ecosystems makes the study of ectotherms complex and challenging. Physiologists have historically overcome this hurdle in the laboratory by using 'average' conditions, representative of the natural environment for any given animal. Temperature, in particular, has widespread impact on the physiology of animals, and it is becoming increasingly important to understand these effects as we face future climate challenges. The majority of research to date has focused on the expected global average increase in temperature; however, increases in climate variability are predicted to affect animals as much or more than climate warming. Physiological responses associated with the acclimation to a new stable temperature are distinct from those in thermally variable environments. Our goal is to highlight these physiological differences as they relate to both thermal acclimation and the 'fallacy of the average' or Jensen's inequality using theoretical models and novel empirical data. We encourage the use of more realistic thermal environments in experimental design to advance our understanding of these physiological responses such that we can better predict how aquatic animals will respond to future changes in our climate.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".