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Record W7100569635

Département de Genie Civil, Université Laval,

2015· article· en· W7100569635 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Drought Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsStatistical analysisMaximum likelihoodStatistical model
DOInot available

Abstract

fetched live from OpenAlex

RESUME L'étude statistique de certaines variables hydrologiques telles que le débit les précipitations, etc. est souvent concentrée sur un aspect particulier de la variable: les valeurs extrêmes. Si les données historiques disponibles sont suffisantes, elles permettront l'ajustement d'une courbe analytique du type classique, offrant ainsi la possibilité d'estimer la fréquence des valeurs à l'extérieur du domaine de l'enregistrement direct. C'est ainsi que l'on peut évaluer la crue centennale, millenale, etc. Le problème que nous abordons dans cet article est le suivant: malgré que la plupart des fonctions utilisées pour l'estimation des valeurs maximales (ou minimales) aient un domaine non borne dans la direction de notre variable, celle-ci possède effectivement des bornes physiques réelles.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.913
Threshold uncertainty score0.892

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0050.002
Open science0.0020.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.3740.139

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 head0.015
GPT teacher head0.219
Teacher spread0.204 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2015
Admission routes1
Has abstractyes

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