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Record W7133271911

Assessment of Scotian Shelf Snow Crab

2024· other· en· W7133271911 on OpenAlexfundaboutno aff
Fisheries and Oceans Canada, Pêches et Océans Canada

Bibliographic record

VenueFederal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du Canada · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersFisheries and Oceans Canada
KeywordsFishingNova scotiaSnowBiomass (ecology)BycatchIchthyoplankton
DOInot available

Abstract

fetched live from OpenAlex

Snow Crab landings from the Scotian Shelf in 2023 were 972 t in north-eastern Nova Scotia (N-ENS); 7,342 t in south-eastern Nova Scotia (S-ENS); and 7 t in crab fishing area (CFA) 4X (season was ongoing at the time of assessment), representing a decrease of 0.3%, an increase of 0.3% and a decrease of 84.2%, respectively, relative to 2022. Total allowable catches (TACs) for 2023 were 979 t, 7,345 t, and 55 t in N-ENS, S-ENS, and 4X, respectively. Bycatch of non-target species is low (<<1% of total catch) in all Snow Crab fishing areas; however, as sampling targets for at-sea observer coverage have not been met in recent years, there is uncertainty in the representativity of the data. In N-ENS, the modelled biomass (pre-fishery) of Snow Crab in 2023 was 3.27 kt, a decline of 10.9% relative to 3.67 kt in 2022. In S-ENS, the modelled biomass (pre-fishery) was 37.91 kt, a decline of 10.6% relative to 42.42 kt in 2022. In 4X, the 2023–2024 season’s modelled biomass (pre-fishery) was 0.08 kt, a decline of 46.7% relative to 0.15 t in the 2022–2023 season. Egg and larval production is expected to be high in 2024 in S-ENS and 4X. Based on length frequency data, little to no recruitment is expected for the next 1–3 years in N-ENS. Continued recruitment is expected for the upcoming years in S-ENS. Low levels of recruitment are expected in 4X; high mortality of adolescent crab makes recruitment into the fishable component uncertain. In 2023, bottom temperatures from the Snow Crab survey have returned to the historical mean in all three CFAs; however, a general warming trend has been observed in the Snow Crab survey since the early 1990s. The amount of viable Snow Crab habitat has consistently declined in all three areas since 2010, and does not show any strong evidence of increase. In 2023, the N-ENS mean estimated fishable biomass was above the upper stock reference (USR), placing the stock in the healthy zone; however, there is considerable model uncertainty around both the estimate and the reference point. In 2023, the harvest rate was 30.17%, slightly above the harvest control rule of 10–30%. A more conservative harvest strategy may support the stock in bridging the expected gap in recruitment. In 2023, the S-ENS mean estimated fishable biomass was above the USR, placing it in the healthy zone; however, there is considerable model uncertainty around both the estimate and the reference point. Harvest rates derived from the fishery model were 19.7% in 2023, and have remained between 10–20% since 2020. The stock status in S-ENS suggests that the current harvest strategy has not been detrimental to the stock. In 2023, the 4X mean estimated fishable biomass was below the limit reference point (LRP), placing the stock in the critical zone. The area is in the southernmost extent of Snow Crab distribution in the North Atlantic and experienced an extended period of unfavorable conditions for Snow Crab in the region.

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 score0.429
Threshold uncertainty score0.863

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 communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.261
Teacher spread0.252 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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Same venueFederal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du Canada→French-language works237,207→