The Importance of Live-Well Transport in the Physiological Disturbance Experienced by Smallmouth Bass in Tournaments on Large Water Bodies
Bibliographic record
Abstract
Abstract Competitive fishing has become an important element of recreational fisheries for black bass Micropterus spp. in North America. The vast majority of competitive events involve a “live-release” format, where fish are held in a boat's live well after being angled and are then released following the weigh-in. We examined the frequency and importance of physical impacts between Smallmouth Bass Micropterus dolomieu and the walls of the live well when tournaments are held on large water bodies. Using an experimental live well that included a video recorder, we determined the number of collisions between Smallmouth Bass and the walls of the live well when a 5.6-m boat was driven on Lake Ontario. During these experiments, 10 of 28 Smallmouth Bass lost equilibrium and became inverted. Live-well transport also resulted in elevations of intracellular enzymes in blood plasma that were used as indicators of cell damage. The results of these experiments indicate that physical impacts with the walls of the live well may be an important factor contributing to the physiological disturbance experienced by Smallmouth Bass in tournaments on large water bodies. Our results also show that this disturbance can be reduced by simply padding the live well. These findings have important implications for fisheries managers, as well as the competitive angling community.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".