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Record W4406933485 · doi:10.1051/climat/202422002

Monographie hydrologique d’Haïti : analyse et prédétermination des pluies et crues fortes à partir de données d’archives et satellitaires

2024· article· fr· W4406933485 on OpenAlexaff
Ralph Bathelemy

Bibliographic record

VenueClimatologie · 2024
Typearticle
Languagefr
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsGeology

Abstract

fetched live from OpenAlex

Cet article est un condensé de la thèse soutenue par Ralph Bathelemy le 14 décembre 2023 et récompensée par le prix Gérard Beltrando de l’Association Internationale de Climatologie au titre de l’année 2024. Cette thèse a été préparée à l’UMR Géoazur (Université Côte d’Azur, France), l’URGéo (Université d’Etat d’Haïti, Port-au-Prince, Haïti) et à l’Unité de recherche HYCAR de l’INRAE (Antony, France) entre 2019 et 2023 sous la direction de Pierre Brigode et de Dominique Boisson.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.351
Threshold uncertainty score0.699

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0080.012
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.038
GPT teacher head0.316
Teacher spread0.277 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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