Largemouth Bass Selected for Differential Vulnerability to Angling Exhibit Similar Routine Locomotory Activity in Experimental Ponds
Bibliographic record
Abstract
Abstract A growing body of work is focused on attempting to understand the biological mechanism(s) by which some fish are highly vulnerable to angling while others are not. We used electromyogram telemetry to monitor routine locomotory activity of artificially selected largemouth bass Micropterus salmoides in experimental ponds to test two potential explanatory hypotheses: (1) that the difference in angling vulnerability between high‐vulnerability (HV) bass and low‐vulnerability (LV) bass is related to a difference in routine activity level between the two groups, and (2) that the difference in vulnerability between HV and LV bass is related to a difference in the diel activity pattern displayed by each group (e.g., LV fish are more active at night, a time where there is typically little bass fishing effort). Neither hypothesis was supported by our results. Differences in vulnerability to angling in artificially selected lines of largemouth bass were not related to inherent differences in routine locomotory activity in our ponds. Mean daily activity levels were close to 5% of maximum swim speed in both groups, which we estimated to reflect a mean swimming distance of approximately 5,875 m (range = 1,280–9,670 m) per day. There was also no difference in the diel pattern of activity displayed by the two groups. Both HV and LV bass displayed a significant diurnal activity pattern: 16% and 19% higher activity levels during the day than at night, respectively. These results contribute to the ongoing efforts to understand the behavioral basis of vulnerability to angling in largemouth bas and other fish species.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".