Metabolic phenotype is not associated with vulnerability to angling in bluegill sunfish (<i>Lepomis</i> <i>macrochirus</i>)
Bibliographic record
Abstract
Prior work has described a link between an individual’s metabolic rate and a willingness to take risks. One context in which high metabolic rates and risk-prone behaviors may prove to be maladaptive is in fish that strike fishing lures only to be captured by anglers. It has been shown that metabolic phenotype may be altered by angling; however, little work has assessed metabolic rate in fish and its relationship to angling vulnerability in a realistic angling trial. To address this, we subjected a set of bluegill sunfish (Lepomis macrochirus Rafinesque, 1819) to a series of angling sessions. Following this, a subset of 23 fish that had been captured at least once and 25 fish that had not been captured were assessed for metabolic phenotype (standard and maximum metabolic rates, postexercise oxygen consumption, and recovery time) via intermittent flow respirometry. Contrary to predictions, captured and uncaptured fish did not differ in any measurement of metabolic rate. These results suggest that metabolic phenotype is not a determinant of angling vulnerability within the studied context. It is possible, therefore, that previously described alterations in metabolic phenotype owing to angling pressure may be context-specific and may not apply to all species and angling contexts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".