Metabolic power budgeting and adaptive strategies in zoology: examples from scallops and fishThe present review is one of a series of occasional review articles that have been invited by the Editors and will feature the broad range of disciplines and expertise represented in our Editorial Advisory Board.
Bibliographic record
Abstract
Evolutionary explanations of the adaptive value of animal characteristics are often expressed in energetic terms, but unless they are accompanied by demonstrations of limited energy availability, they remain speculative. In this review, we argue that metabolic power budgeting provides easily testable mechanisms through which energetically efficient attributes could become adaptive. Given each organism’s maximal aerobic (and metabolic) capacity, available metabolic power (energy use per unit time) is limited and must be partitioned between different processes. This leads to compromises among the major fitness functions of growth, locomotor activity, and reproductive investment. As examples of such conflicts, we examine the compromise among growth, reproduction, and predator avoidance in scallops, as well as the means whereby thermal limitations on oxygen uptake reflect the geographical distribution limits and associated energetic trade-offs of temperate zone and polar fishes. These examples show several means whereby the budgeting of aerobic power is implicated in the major fitness trade-offs faced by animals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".