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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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.404
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.004
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.003

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; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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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