Pragmatic perspective on aerobic scope: peaking, plummeting, pejus and apportioning
Bibliographic record
Abstract
A major challenge for fish biologists in the 21st century is to predict the biotic effects of global climate change. With marked changes in biogeographic distribution already in evidence for a variety of aquatic animals, mechanistic explanations for these shifts are being sought, ones that then can be used as a foundation for predictive models of future climatic scenarios. One mechanistic explanation for the thermal performance of fishes that has gained some traction is the oxygen and capacity-limited thermal tolerance (OCLTT) hypothesis, which suggests that an aquatic organism's capacity to supply oxygen to tissues becomes limited when body temperature reaches extremes. Central to this hypothesis is an optimum temperature for absolute aerobic scope (AAS, loosely defined as the capacity to deliver oxygen to tissues beyond a basic need). On either side of this peak for AAS are pejus temperatures that define when AAS falls off and thereby reduces an animal's absolute capacity for activity. This article provides a brief perspective on the potential uses and limitations of some of the key physiological indicators related to aerobic scope in fishes. The intent is that practitioners who attempt predictive ecological applications can better recognize limitations and make better use of the OCLTT hypothesis and its underlying physiology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".