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Record W7109165884 · doi:10.60825/937c-xc77

Indicators for monitoring fish and invertebrate communities sampled in the annual ecosystem survey in the northern Gulf of St. Lawrence

2025· report· en· W7109165884 on OpenAlexaff

Bibliographic record

VenueFisheries and Oceans Canada / Pêches et Océans Canada - Publications · 2025
Typereport
Languageen
Field
Topic
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsInvertebrateSpecies richnessHabitatBiomass (ecology)EcosystemTemperate climateBiodiversitySubmarine pipelineProductivity

Abstract

fetched live from OpenAlex

The northern Gulf of St. Lawrence (nGSL) has undergone significant ecological and environmental changes over the past decades. To date, the impact of these changes on offshore fish and invertebrate communities, as well as on the ecosystem functions they support, remains poorly documented. This report presents a first suite of ecological indicators that will aid in understanding, monitoring, and better anticipating changes in the structure and functioning of offshore communities in the nGSL. These indicators reveal unstable communities undergoing restructuring following the considerable increase in redfish biomass and the persistent rise in water temperatures. These ecological and environmental changes have also contributed to shifting habitat use patterns since 2017 and pose a risk to the persistence of mixed communities composed of both boreal and temperate species in the nGSL. The substantial reduction of energy fluxes associated with micronekton, the low productivity of invertebrate species, the recent decrease in the biomass of fish and in the species richness of small to medium-sized organisms, as well as the poor condition of several fish species, all confirm that offshore communities are currently vulnerable to disturbances.

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 score0.670
Threshold uncertainty score0.657

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.052
GPT teacher head0.262
Teacher spread0.210 · 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 venueFisheries and Oceans Canada / Pêches et Océans Canada - PublicationsFrench-language works237,207