Selection for Vulnerability to Angling in Largemouth Bass
Bibliographic record
Abstract
Abstract Although a great deal of effort has been expended to try to understand the consequences of fishing‐induced selection by commercial fisheries, relatively little effort has been put into trying to understand the selective effects of recreational angling. We conducted a long‐term selection experiment to assess the heritability of vulnerability to angling in largemouth bass Micropterus salmoides. Three successive generations of artificially selected largemouth bass were produced from a single experimental study population. Within each generation, individual adult largemouth bass were identified as having either high or low vulnerability to angling through a series of controlled catch‐and‐release angling trials. Individuals of each vulnerability group (high and low) were then selected from that population for breeding to produce the next generation. The response to selection for vulnerability to angling increased with each generation; that is, the magnitude of the difference between the high‐ and low‐vulnerability groups of fish increased with each successive generation. Realized heritability was calculated as 0.146 (r2 = 0.995), indicating that the vulnerability of largemouth bass to angling is indeed a heritable trait. Our results indicate that recreational angling has the potential to alter the gene pool of wild fish populations, which may indirectly affect population characteristics such as survival, growth rate, and reproductive output as well as directly affecting angling success rates.
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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".