MétaCan
Menu
Back to cohort
Record W4380360657 · doi:10.1139/facets-2022-0227

Baseline composition, quantity, and condition of bycatch in the southern Gulf of St. Lawrence spring and summer American lobster fisheries

2023· article· en· W4380360657 on OpenAlexaffvenue
Stephanie A. Boudreau, Patricia H. Hanley

Bibliographic record

VenueFACETS · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsBycatchAmerican lobsterFisheryHomarusFishingInvertebrateGeographyBiologyCrustacean

Abstract

fetched live from OpenAlex

The impact of the southern Gulf of St. Lawrence American lobster ( Homarus americanus) fishery on species bycatch is currently unknown. The composition of the incidental catch, both nonharvestable lobster (by fisheries regulations) and nonlobster species, was systematically collected over the 2015 spring and summer fishing seasons. A total of 51 948 (7147 were nonlobster taxa) individual organisms weighing 13 987.60 kg (1223.91 kg of nonlobster taxa) were captured as bycatch during 73 fishing trips. By weight per trip, the most common lobster bycatch were undersized male and females, and the highest nonlobster species catch were Atlantic rock crab ( Cancer irroratus). A semiquantitative assessment of injury and vitality was applied to bycatch as a proxy for discard mortality. The majority of the individuals assessed for visible injury were deemed uninjured (98% both fish and invertebrates); however, postrelease mortality was not measured. A smaller study in 2019 corroborated the 2015 catches and supported current assumptions that the passive gear type, the low diversity of bycatch, and the rapid hand-sorting of the trap minimize the impact of the lobster fishery on incidentally captured taxa. Further scientific monitoring is recommended to better account for all sources of mortality in stock assessments and rebuilding plans.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.027
GPT teacher head0.277
Teacher spread0.250 · 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 teacher head, 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

Citations5
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueFACETSSame topicMarine and fisheries researchFrench-language works237,207