A comparison of methods for estimating activity costs of wild fish populations: more active fish observed to grow slower
Bibliographic record
Abstract
Activity costs can account for a major proportion of fish energy budgets and may trade off against observed growth rates in wild fish populations. Recent approaches to estimating activity costs in situ have used a contaminantbioenergetic mass balance modelling approach, allowing for a broader examination of activity costs among populations compared with time-consuming alternative approaches. We report the results of this contaminantbioenergetic modelling approach to estimating in situ activity costs compared with two alternative independent methods of assessing in situ activity costs. Comparisons were made between a fast- and slow-growing yellow perch (Perca flavescens) population. Contaminantbioenergetic estimates of activity costs in the fast-growing population were 39% lower than those in the slow-growing population. Activity estimated from recorded swimming behaviours was 37% lower in the fast-growing population and 22%29% lower in the fast-growing population based on published relationships between activity costs and axial white muscle glycolytic enzyme capacities. Consumption rates were actually 32% lower in the fast-growing population, implying that lower activity costs more than compensated for lower food intake. The agreement among the three independent measures of activity costs strongly support the idea that activity costs, rather than food intake, are a major determinant of growth differences in these two wild fish 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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".