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Record W7147739702 · doi:10.26071/5af00808-5875-4841

CTD Data for the RQM-MEOPAR COR2001 Cruise (2020) in the Saint-Lawrence Estuary

2025· dataset· fr· W7147739702 on OpenAlexaffabout
Jean-Carlos Montero-Serrano, Audrey Limoges, Alexandre Normandeau, Anne Corminboeuf, Tina Laphengphratheng

Bibliographic record

VenueOGSL repository · 2025
Typedataset
Languagefr
Field
Topic
Canadian institutionsUniversité du Québec à RimouskiGeological Survey of Canada
Fundersnot available
KeywordsCruiseCoringBathymetryEstuaryCTDSampling (signal processing)Sediment

Abstract

fetched live from OpenAlex

Based on a transdisciplinary approach, the purpose of the 2020-RQM-MEOPAR funded expedition (cruise COR2001 id : 2020_31) is to carry out CTD-rosette measurements, hydroacoustic surveys (high-resolution multibeam bathymetry and sub-bottom profiles), sediment coring operations, plankton net sampling and collect geopoetics and literary material and collect geopoetics and literary material. The operations took place in the St-Lawrence estuary (Québec Canada) between Pessamit and Sainte-Anne-des-Monts. This dataset is part of the EDMS-ISMER-QO collection

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.858
Threshold uncertainty score0.282

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.008
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0440.063

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 head0.309
Teacher spread0.276 · 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 designNot applicable
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
Published2025
Admission routes2
Has abstractyes

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