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, hydroelectric power production has only been developed on large basins, which are the only ones that have been gauged. This excludes innumerable small catchments, for which only rainfall data are available. Therefore, the objective of the work presented in this paper is to develop a hydrological rainfall–runoff model to simulate flow duration curves for hydro power production planning. The model is based on a linear and time-invariant system (input–output). The impulse response of the system is calculated from the cross-spectral analysis between the rainfall and runoff series. This response is optimized successive approximations to minimize the root mean square of the error. The test catchment area has 7 years of rainfall and runoff data; 4 years are considered for the calibration and 3 years for the validation of the model. A sensitivity analysis of the model to the sample size is carried out to determine the shortest possible data period that produces results comparable to those of the model validation. Key words: model rainfall–runoff, small catchments, Amazonia, impulse response, sensitivity analysis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".