Efficient watershed modeling using a multi-site weather generator for meteorological data
Bibliographic record
Abstract
The multi-site generation of precipitation data is developed using a Richardson (1981) WGEN-type weather generator. This approach is based on spatial autocorrelation to analyze patterns in space and investigate the dependence of weather data at multiple locations. Reproducing the dependence between meteorological data at several stations should make the hydrological model results more realistic. The Chute du diable watershed and surrounding area located in the province of Quebec, Canada was used to test the proposed approach. Daily spatial autocorrelations between precipitation occurrences and amounts were successfully reproduced as well as total monthly precipitation and monthly numbers of rainy days. A hydrological model has been used to quantify the natural inflow process. As envisaged, the multi-site generation of weather data produced more practical natural inflow hydrographs, compared to those obtained using a uni-site weather generator.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".