Fishery-Scale Discard Mortality Rate Estimate for Haddock in the Gulf of Maine Recreational Fishery
Bibliographic record
Abstract
Abstract Empirical discard mortality rate estimates are vital to both stock assessments and fishery management, especially for stocks that experience high discard rates, such as in the recreational rod-and-reel fishery for Haddock Melanogrammus aeglefinus in the Gulf of Maine. The objective of the present study was to derive a fishery-scale discard mortality rate estimate for Haddock that are captured and released in the Gulf of Maine recreational fishery by combining results of an electronic-tagging telemetry experiment with representative fishery-dependent survey data. Scientific personnel and industry partners collected data on a suite of biological, environmental, and technical covariates from 2,442 Haddock caught under authentic fishery scenarios during 2015. Despite being a physoclistous species, <1% of sampled Haddock were observed to die when brought onboard and only ~3% floated upon release. Postrelease fate was then monitored for 154 Haddock using passive acoustic telemetry and determined using a semiquantitative classification procedure reliant upon movement data of Haddock with known fates. The resulting data were analyzed with a parametric survival model to identify which capture-related covariates influenced mortality. Fishing season and length-class of Haddock were the most significant predictors of discard mortality, with increased mortality for smaller individuals caught during the autumn, possibly due to increased temperatures. Survival modeling identified that mortality from these covariates occurred primarily after release as compared with during capture and handling. By integrating survival modeling results with fishery-dependent observations, a fishery-scale discard mortality rate of 63% was estimated for the 2015 fishing year. Based on these findings, we recommend that fishery managers implement measures to reduce recreational Haddock discards, especially of smaller Haddock during warmer months.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".