Probabilistic Forecasting of Hydrological Events Using Geostatistical Analysis
Bibliographic record
Abstract
Une methode de prevision probabiliste d'evenements hydrologiques, basee sur une analyse geostatistique, est introduite. Dans cette methode, les predicteurs d'une variable hydrologique definissent un champ virtuel tel que, au sein de ce champ, les variables observees dependantes sont considerees comme des points de mesure. La variographie des points de mesure permet d'utiliser la methode de krigeage pour estimer la valeur de la variable en des lieux du champ qui ne disposent pas de mesure. Les points sans mesure sont les previsions, associees a des predicteurs specifiques. Le calcul de la variance d'estimation facilite l'analyse probabiliste des variables de prevision. La methode est appliquee aux etudes de cas de la Riviere Red au Manitoba, au Canada, et de la Riviere Karoon au Khoozestan, en Iran. L'etude analyse les avantages et les limites de la methode proposee par comparaison avec une approche des K plus proches voisins et avec des regressions multiples lineaire et non-lineaires. L'utilite de la methode proposee pour la prevision de variables hydrologiques, avec la distribution de probabilite conditionnelle associee, est demontree.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".