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Record W2293186592 · doi:10.3166/isi.20.3.143-167

Un système décisionnel pour l’analyse de la qualité des eaux de rivières

2015· article· fr· W2293186592 on OpenAlexvenueno aff
Sandro Bimonte, Kamal Boulil, Agnès Braud, Sandra Bringay, Flavie Cernesson, Xavier Dolques, Mickaël Fabrègue, Corinne Grac, Nathalie Lalande, Florence Le Ber, Maguelonne Teisseire

Bibliographic record

VenueIngénierie des systèmes d information · 2015
Typearticle
Languagefr
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
FundersOffice National de l’Eau et des Milieux Aquatiques
KeywordsPolitical scienceForestryGeographyHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

/ Cet article décrit un système décisionnel développé pour permettre l'analyse des données concernant le fonctionnement des hydro-écosystèmes ; ces données sont nombreuses, diverses et issues de sources variées. Le système mis en place comporte une base de données intégrée, un entrepôt permettant l'exploration des dimensions associées aux données, et des outils de fouille permettant de répondre aux questions des hydro-écologues

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)0.010
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.027
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0070.003
Science and technology studies0.0020.001
Scholarly communication0.0080.005
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.002

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 head0.037
GPT teacher head0.290
Teacher spread0.253 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2015
Admission routes1
Has abstractyes

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