Venting and Descending Provide Equivocal Benefits for Catch-and-Release Survival: Study Design Influences Effectiveness More than Barotrauma Relief Method
Bibliographic record
Abstract
Abstract Descending fish to depths of neutral buoyancy is a promising, less-invasive alternative to swim bladder venting for relieving barotrauma and reducing mortality in sport fish. However, we lack a broad perspective on the relative benefits of these two approaches. We reviewed the most up-to-date literature to evaluate the effectiveness of venting compared to descending treatments. Mean relative risk (RR) based on 76 published comparisons (51 marine, 25 freshwater; 18 genera, 28 species) showed that venting (2.0 ± 4.7 [mean ± SD]) and descending (1.6 ± 1.4) both had positive effects on survival (RR ≥ 1.1). However, RR was generally small and statistically indistinguishable between treatments, providing no strong support for the use of one method over the other. Modeling of factors affecting RR showed that the study design variable “assessment method” was the only important factor affecting RR, having a larger influence on survival than habitat, capture depth, or treatment type (venting versus descending). Biotelemetry and ex situ methods produced significantly higher estimates of RR than other assessments. Our review suggests that the two major approaches to barotrauma relief do not differentially influence survival outcomes and that study design may be an important source of bias. Consequently, we recommend that managers consider barotrauma relief options carefully on a case-by-case basis, and we encourage additional research on sublethal endpoints in addition to mortality. Received November 29, 2016; accepted March 9, 2017 Published online May 4, 2017
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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 | 0.108 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".