Estimating and mitigating the discard mortality of Atlantic cod (Gadus morhua) in the Gulf of Maine recreational rod-and-reel fishery
Bibliographic record
Abstract
Abstract In recent years, the recreational contribution to the total catch of Atlantic cod (Gadus morhua) in the Gulf of Maine (GOM) has increased with recreational discards outnumbering recreational landings by 2:1. However, the discard mortality (DM) rate of cod released in the recreational fishery remains poorly understood, thus contributing to the uncertainty in stock assessments and fishery management plans. The current study examined the capture-related factors most detrimental to cod DM in the GOM recreational rod-and-reel fishery. Atlantic cod (n = 640; 26.0–72.0 cm) were angled from June–October 2013 on southern Jeffreys Ledge in the western GOM using fishing gear representative of the local recreational fishery. A subset (n = 136) was also tagged with pressure-sensing acoustic transmitters before being released into an acoustic receiver array (n = 31) deployed to monitor survival up to 94 days. To properly model DM up to the fishery-wide level, all cod were visually assessed for capture-related injuries according to a four-level injury score index. Mean tackle-specific DM rates of 15.4 and 21.2% were estimated for bait- and jig-captured cod, respectively, with an overall 16.5% mean DM rate for the 2013 GOM recreational cod fishery. Twenty-nine cod tagged with acoustic transmitters were identified as dead, where the majority (∼90%) died within 16 h post-capture. Upon evaluation with a specifically adapted parametric survival analysis, greater incidence of mortality was attributed to the capture and handling process (rather than release) for moderately and severely injured cod. Based on the capture-related factors associated with the highest injury rates, we recommend minimizing fight and handling times, avoiding areas with small cod, educating inexperienced anglers, and favouring bait over jigs to mitigate mortality. Results will continue to inform the development of fishery management plans and enhance survival through dissemination of “best practice” techniques to fishery stakeholders.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".