Behavioral phenotype does not predict habitat occupancy or angling capture of largemouth bass (<i>Micropterus</i><i> salmoides</i>)
Bibliographic record
Abstract
Fish behavior types can predict angling vulnerability, providing insights about how recreational fishing may lead to artificial trait selection. Most vulnerability studies have focused on species with active foraging strategies, and the impact of environmental conditions on vulnerability has not been quantified. The objective of this study was to determine the influences of behavior types and habitat on angling vulnerability of largemouth bass (Micropterus salmoides (Lacepède, 1802)) — a sit-and-wait predator. Behavior assays quantified individual activity and boldness, then experimental angling took place in ponds with two habitat treatments: (1) structured habitat with artificial structures present and (2) open habitat with no structures added. Two anglers determined which individual largemouth bass were vulnerable to capture across the two contexts. In contrast with previous studies involving active foragers, behavior types of largemouth bass did not influence capture, regardless of habitat type. The number of captures also did not differ between structured and open habitat. However, anglers captured fish with different behavioral phenotypes, revealing additional complexity for factors that may affect behavioral selection. Findings suggest that angling may not be selecting for specific activity or boldness phenotypes of largemouth bass, even across habitat types, but that anglers may influence selection.
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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".