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Record W6912999350 · doi:10.5683/sp3/rg0lle

Données de géophysique acquises dans le cadre du Projet d'acquisition de connaissances sur les eaux souterraines (PACES) aux îles de la Madeleine

2022· dataset· fr· W6912999350 on OpenAlexaffabout

Bibliographic record

VenueBorealis · 2022
Typedataset
Languagefr
Field
Topic
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsVettingStatistical analysisProfessional qualification

Abstract

fetched live from OpenAlex

Dans le cadre du Projet d'acquisition de connaissances sur les eaux souterraines (PACES) aux îles de la Madeleine, des travaux de géophysique ont été réalisés conjointement entre l’INRS-ETE et l’Université Laval en 2019. Au total, 693 mesures de la méthode électromagnétique transitoire (TDEM) ont été réalisées. L’objectif de ces travaux était de documenter l’hétérogénéité des grès du Membre de l’Étang-des-Caps, qui constitue le milieu aquifère principal de l’archipel, et localiser l’interface entre l’eau douce et l’eau salée en profondeur sur les différentes îles habitées, excluant l’île d’Entrée. Des diagraphies ont aussi été réalisées dans des puits municipaux, des puits privés et des puits du RSESQ. Les données de diagraphie sont ici disponibles en format .wcl et .las et les données de la méthode électromagnétique transitoire (TDEM) sont disponibles en format .mat.

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)0.004
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.938
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.006

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 head0.027
GPT teacher head0.270
Teacher spread0.243 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

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