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Record W6945683924 · doi:10.25976/etrh-6235

RSMA-données RUISSO annuelles

2025· dataset· fr· W6945683924 on OpenAlexaboutno aff

Bibliographic record

VenueDataStream · 2025
Typedataset
Languagefr
FieldEngineering
TopicReal estate and construction management
Canadian institutionsnot available
Fundersnot available
KeywordsQuality (philosophy)Water qualityWater resourcesWork (physics)

Abstract

fetched live from OpenAlex

Le programme RUISSO du RSMA (Réseau de suivi du milieu aquatique) vise à documenter la qualité de l’eau des ruisseaux et des plans d’eau intérieurs de l’agglomération montréalaise, selon des analyses bactériologiques et physicochimiques. - The RUISSO program of the RSMA (Réseau de suivi du milieu aquatique) aims to document the water quality of streams and inland water bodies in the Montreal agglomeration, based on bacteriological and physicochemical analyses.

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)0.015
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 score0.058
Threshold uncertainty score0.194

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0040.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0580.085

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 head0.009
GPT teacher head0.224
Teacher spread0.215 · 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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