Application of a parametric survival model to understand capture-related mortality and predation of yellowfin tuna (<i>Thunnus albacares</i>) released in a recreational fishery
Bibliographic record
Abstract
Distinguishing the cause and magnitude of capture-related mortality (CRM) in fishes is important for effective management. To better understand CRM in yellowfin tuna (Thunnus albacares) released in the recreational troll fishery off the United States east coast, 48 fish (76–127 cm curved fork length, CFL) were monitored for up to 86 days postrelease with survivorship pop-up satellite archival tags. Recovered data indicated 40 fish were alive at the time of tag detachment and eight died, including six from predation, from 0 to 30 days postrelease. Survival model variants were constructed to independently estimate the rates of immediate capture and handling (CH), postrelease (PR), total CRM (CH + PR), and natural mortality (M) for small (≤103 cm CFL) and large (>103 cm CFL) fish under different hypotheses and causes of mortality. CH was low (0%–8%) for both size classes but predation was an important component of PR, particularly in the small size class. Total CRM was 51% (95% CI: 26%, 81%) for small and 8% (95% CI: 2%, 30%) for large fish.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".