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Record W6945735574 · doi:10.25976/mljk-rz28

RSMA – données QUALO annuelles

2025· dataset· fr· W6945735574 on OpenAlexaboutno aff

Bibliographic record

VenueDataStream · 2025
Typedataset
Languagefr
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsWater qualityWater resourcesQuality (philosophy)Fecal coliformHydrology (agriculture)

Abstract

fetched live from OpenAlex

Le programme QUALO du RSMA (Réseau de suivi du milieu aquatique) vise à documenter la qualité de l’eau en rive du fleuve St-Laurent dans la région montréalaise selon des mesures du pH, de la température, de la conductivité et des coliformes fécaux. - The QUALO program of the RSMA (Réseau de suivi du milieu aquatique) aims to document the water quality along the banks of the St. Lawrence River in the Montreal area based on measurements of pH, temperature, conductivity and fecal coliforms.

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)0.002
metaresearch head score (Gemma)0.018
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 score0.071
Threshold uncertainty score0.238

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.018
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0070.009
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0040.003
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0710.107

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 head0.022
GPT teacher head0.289
Teacher spread0.266 · 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 routes1
Has abstractyes

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Same venueDataStreamFrench-language works237,207