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Record W2968112050 · doi:10.1002/nafm.10328

Fishery-Scale Discard Mortality Rate Estimate for Haddock in the Gulf of Maine Recreational Fishery

2019· article· en· W2968112050 on OpenAlexaff
Connor W. Capizzano, Douglas R. Zemeckis, William S. Hoffman, Hugues P. Benoît, Emily Jones, Micah J. Dean, Nate Ribblett, James A. Sulikowski, John W. Mandelman

Bibliographic record

VenueNorth American Journal of Fisheries Management · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans Canada
FundersNational Marine Fisheries ServiceSchool for Marine Science and Technology, University of Massachusetts Dartmouth
KeywordsHaddockFisheryFishingRecreational fishingGadidaeRecreationStock assessmentCommercial fishingAtlantic codGeographyEnvironmental scienceBiologyEcologyFish <Actinopterygii>Gadus

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.236
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2019
Admission routes1
Has abstractyes

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