Temperature affects fish body sizes. Which sizes?
Bibliographic record
Abstract
An extensive literature exists on how environmental conditions, especially temperature, impact animal body sizes. However, there remains considerable discrepancies, and misunderstanding, in the key definitions and concepts of body size used to describe observed impacts across studies. Size can be measured using continuous growth metrics, including von Bertalanffy growth coefficients, or static 'size' metrics, such as population-averaged length or mass, average size-at-(arbitrary)-age, size-at-maturity, adult size, asymptotic size, or the maximum observed size. Critically, these concepts of size are not equivalent, and temperature is likely to affect each in different ways. The use of these disparate size and growth metrics as response variables estimated across different biological scales (individual, population, or community) and empirical contexts (laboratory, field) has led to unnecessary confusion and apparent contradictions among practitioners. Here, we review nine common confusions associated with the measurement of 'size' in fish and other water-breathing ectotherms. We then highlight outstanding knowledge gaps on how temperature and global warming might affect different size metrics. Clarifying concepts, definitions, and applications of body size measures is important as it can help reconcile divergent findings, target future research, and improve our predictions about the warming impacts on wild populations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".