Transférabilité d’un modèle pluie–débit pour la simulation de courbes de débits classés sur des petits bassins non jaugés de l’Amazonie
Bibliographic record
Abstract
In Amazonia, because the small catchments are ungauged, it is not possible to analyse them, for example, for hydroelectric power production. Thus, the objective of this paper is to study the transferability of a rainfall–runoff model to simulate flow duration curves for the production of hydroelectric power. The approach is based on the transfer of the impulse response of a model calibrated on two gauged catchments, allowing the evaluation approach permutation between these two catchments. We have, respectively, 7 years and 2 years and 2 months of rainfall and runoff data for these catchments. A sensitivity analysis of the transferability calibration to the sample size is carried out to determine the shortest flow period gauged on the receptor catchment, which produces results comparable to those calibrated with the maximum samples size. This analysis evaluates fieldwork on the ungauged sites of the region.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".