A review of the effects of catch‐and‐release angling on black bass, <i>Micropterus</i> spp.: implications for conservation and management of populations
Bibliographic record
Abstract
Abstract This paper summarises recent peer‐reviewed literature addressing the effects of catch‐and‐release angling on black bass, Micropterus spp., to facilitate management and conservation of these fish. Traditionally, the effects of catch and release have been evaluated by measuring mortality. Many recent studies have measured sublethal effects on physiology and behaviour. There is also greater emphasis on adding more realism to sublethal catch‐and‐release experiments through angler involvement in research activities and by conducting studies in the field rather than in laboratory environments. Owing to these advances, there have been a number of recent findings, which are summarised here, related to air exposure, gear (e.g. circle hooks) and the weigh‐in procedure that are particularly relevant to black bass anglers, tournament organisers and fishery managers. Additional research is particularly needed for: (1) population‐level effects of angling for nesting fish; (2) population‐level effects of tournament‐associated mortality; (3) effectiveness of livewell additives for enhancing survival; (4) consequences of fish displacement in competitive events; (5) effects of weigh‐in procedures and other organisational issues on fish condition and survival; and (6) reducing barotrauma.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".