Fishes in Warming Waters, the Gill-Oxygen Limitation Theory and the Debate Around Mechanistic Growth Models
Bibliographic record
Abstract
Mechanistic explanations of the impact of climate change on fish growth are currently under debate. However, critical assessments of even the most prominent theories are not always based on accurate interpretations of their underlying mechanistic models. This contribution addresses some of the major misunderstandings still causing the Gill-Oxygen Limitation Theory (GOLT) from being examined based on its actual structuring elements and assumptions, rather than erroneous perceptions. As we argue, recent critiques of the GOLT are based on implausible interpretations of respirometry data that are invoked to distinguish maintenance costs and overhead costs of growth. Discussing the current state of the debate, we emphasize the fact that fasting young and, thus, growing fish for short periods of time is not sufficient to suppress energy (i.e., oxygen) allocation to growth. In the process of dealing with these issues, several cases of apparent ‘counter-evidence’ are discussed. Highlighting the need to base critical discussions and examinations of the GOLT on its actual predictions, we recommend that testing the theory should focus on broad reviews or meta-analyses, e.g., on datasets of gill surface area and the relationship of these data to growth performance under different temperature regimes.
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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 | 0.020 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| 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".