Estimation régionale par la méthode de l'analyse canonique des corrélations: comparaison des types de variables hydrologiques
Bibliographic record
Abstract
It is often necessary to estimate extreme events at sites where little or no hydrometric data are available. In such cases, one may use a regional estimation procedure, utilizing data available from other sites in the same hydrologic region. In general, a regional flood frequency procedure consists of two steps: determination of hydrologically homogeneous region and regional estimation. This paper presents the development of a regional flood frequency procedure based on canonical correlation analysis and multiple regression: (i) the canonical correlation analysis allows us to link a set of hydrological variables and a set of physiographical and (or) meteorological variables and, therefore, to determine the hydrological neighborhood of an ungauged site; (ii) the multiple regression allows us to transfer data from sites of the homogeneous region to the target site. The developed methodology was applied to the Saint-Maurice river region (Quebec, Canada). Using a jackknife procedure, several pairs of hydrological variables were compared to identify the most appropriate grouping. Key words: regionalization, frequency analysis, canonical correlation analysis, multiple regression.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".