The metabolic and biochemical basis of vulnerability to recreational angling after three generations of angling-induced selection in a teleost fish
Bibliographic record
Abstract
Although the selective pressures of commercial fishing are well known, few studies have examined this phenomenon in recreational fisheries. This study used a unique population of largemouth bass ( Micropterus salmoides ) with lines bred for low (LVF) and high (HVF) vulnerability to recreational angling. We evaluated whether differential vulnerability to angling was correlated with physiological traits, including metabolic rate, metabolic scope, anaerobic capacity, and biochemical response to exercise. Indeed, angling selection affected the metabolic rate of fish significantly. The standard metabolic rate was 10%, maximal metabolic rate was 14%, and metabolic scope was 16% lower for LVF compared with HVF. Following exhaustive exercise, LVF required 1 h for lactate levels to recover to control values, whereas HVF required 2 h. Anaerobic energy expenditure was significantly lower for LVF, a finding consistent with the observation that LVF swam at a steadier rate during exercise. Although the reasons behind vulnerability to angling are complex, the phenotypic trait “vulnerability to angling” appears to be linked to a suite of physiological traits, including metabolism and the capacity for anaerobic activity. Thus, angling-induced selection might alter the physiological characteristics of wild largemouth bass populations, with unknown outcomes for long-term population viability.
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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 | 0.000 |
| 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".