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Record W7133289386

Assessment of Scotian Shelf Snow Crab in 2023

2025· other· en· W7133289386 on OpenAlexaboutno aff
K. L. Christie, B. J. Cameron, A. C. Glass, J. S.‏ Choi

Bibliographic record

VenueFederal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du Canada · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsGroundfishNova scotiaFishingSnowHabitatSea surface temperatureHaddockBycatch
DOInot available

Abstract

fetched live from OpenAlex

In the Scotian Shelf Ecosystem, Snow Crab (Chionoecetes opilio) have been a dominant macro-invertebrate since the decline of the groundfish fishery in the 1990s. They are generally observed in deep, soft-bottom substrates ranging from 60-300 m and at temperatures usually less than 6 degrees Celsius. The Scotian Shelf Snow Crab are in the southern-most extreme of their spatial distribution in the Northwest Atlantic Ocean and vulnerable to climate variability. The fishery is made up of 3 fishing areas: north-eastern Nova Scotia (N-ENS), south-eastern Nova Scotia (S-ENS) and 4X. The Snow Crab assessment is based on a fishery independent survey, the Maritimes Region Snow Crab Trawl Survey, and focuses on indicators of abundance, reproductive potential, recruitment, and exploitation rates. Spatiotemporal models incorporate habitat viability based on ecosystem variables such as depth, species composition and bottom temperatures. At-sea observer coverage aims to quantify bycatch, and carapace conditions for the crabs in the fishery but has been unreliable since 2020 with minimal observations per year. Fishery data provides catch rates, landings, effort, and monitors historic trends. Total allowable catches (TACs) are generally caught every year (except 4X as the fishery is always ongoing at the time of assessment). Survey indices indicate recruitment in N-ENS will be minimal with a potential gap for the next 1 to -3 years. In S-ENS, recruitment is likely to continue at a moderate rate in the upcoming season. 4X is expected to see low to moderate levels of recruitment for two years. Fishing mortality was higher than desirable in N-ENS. Bottom temperatures have been continually rising since the early 2000s until a decrease observed during the 2023 survey. This is positive news for the Snow Crab fishery and will be monitored with future surveys in hopes this decreasing trend continues. The model suggests that N-ENS and S-ENS are in the healthy zone and 4X is in the critical zone.

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)0.000
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 score0.695
Threshold uncertainty score0.607

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.008
GPT teacher head0.259
Teacher spread0.251 · 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
Published2025
Admission routes1
Has abstractyes

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