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Record W6962696658 · doi:10.15468/fndf7x

DFO Quebec Region Biodiversity of the snow crab trawl survey in the St. Lawrence Estuary (2019)

2022· dataset· fr· W6962696658 on OpenAlexaffabout

Bibliographic record

VenueGlobal Biodiversity Information Facility · 2022
Typedataset
Languagefr
Field
Topic
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsEstuaryBiodiversityBenthic zoneHabitatSnowAbundance (ecology)

Abstract

fetched live from OpenAlex

A research survey on snow crab (Chionoecetes opilio), using a beam trawl, was carried out by DFO in 2019 in the St. Lawrence Lower Estuary between Forestville, Baie-Comeau and Matane. The main objective of this survey was to assess crab abundance and the diversity of benthic species associated with the crab habitat according to a fixed station sampling plan. The data provided is a compilation by species (or taxon), by station. The taxonomic and geographic validity of the records were verified. Un relevé de recherche sur le crabe des neiges (Chionoecetes opilio), à l'aide d'un chalut à perche (connu aussi sous le nom de chalut à bâton), a été réalisé par le MPO en 2019 dans l’estuaire maritime du Saint-Laurent entre Forestville, Baie-Comeau et Matane. L’objectif principal de ce relevé était d’évaluer l’abondance du crabe et la diversité des espèces benthiques associées à l’habitat du crabe selon un plan d'échantillonnage à stations fixes. Les données fournies sont une compilation par espèce (ou taxon), par station. La validité taxonomique et géographique des enregistrements a été vérifiée.

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: Dataset · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.001

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 head0.033
GPT teacher head0.223
Teacher spread0.190 · 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
GenreDataset

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
Published2022
Admission routes2
Has abstractyes

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