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Record W2081380198 · doi:10.2960/j.v36.m552

Changes in the Abundance and Size of Skates in the Southern Gulf of St. Lawrence, 1971-2002

2005· article· en· W2081380198 on OpenAlexaff
Douglas P. Swain, Thomas Hurlbut, Hugues P. Benoît

Bibliographic record

VenueJournal of Northwest Atlantic Fishery Science · 2005
Typearticle
Languageen
FieldPsychology
TopicAdventure Sports and Sensation Seeking
Canadian institutionsFisheries and Oceans Canada
FundersDartmouth College
KeywordsAbundance (ecology)OceanographyFisheryGeologyGeographyBiology

Abstract

fetched live from OpenAlex

Three species of skates commonly occur in the southern Gulf of St. Lawrence: thorny skate Amblyraja radiata, winter skate Leucoraja ocellata, and smooth skate Malacoraja senta. Trends in their abundance and size are described using data from annual bottom-trawl surveys conducted each September since 1971. Biomass and the abundance of mature skates decreased over the 1971-2002 period, by 80-90% for thorny and winter skates, and to a lesser degree for smooth skates. Abundance of juvenile thorny and smooth skates increased from the mid-1980s to a peak in the mid-1990s, and then declined in the late1990s. Mean length decreased by 20-30% during the 1980s for each of the three species. The increase in the abundance of juvenile skates in the 1990s coincided with a collapse in the biomass of large-bodied demersal teleost fishes, a dramatic decline in fishing effort, a cooling of the cold intermediate layer in the southern Gulf and decreasing abundance of mature skates. The decline in the abundance of large skates may be an effect of fishing, though reported landings of skates have been low. These results for the southern Gulf of St. Lawrence contrast those observed on Georges Bank and in the North Sea, where small elasmobranch species that were not targeted by fisheries increased in biomass as the biomass of heavily exploited groundfish stocks declined.

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)0.002
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 score0.030
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.021
GPT teacher head0.278
Teacher spread0.257 · 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

Citations12
Published2005
Admission routes1
Has abstractyes

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