Modélisation non paramétrique de la relation entre les caractéristiques du vent et la différence de niveaux sur un grand réservoir
Bibliographic record
Abstract
The natural inflow at a site is a key variable for optimal management of water resources, particularly for hydroelectric production. For sites with dams and hydroelectric powerplants, this variable cannot be measured directly, and the water balance equation is used to determine the quantity of water a site receives on its surface during a certain period of time. However, several errors affect the natural inflows computed this way. One of the principal sources of uncertainty for large reservoirs is the nonrepresentativeness of water level because of the wind effect. To quantify the effect of wind on the reservoir surface, a nonparametric regression model was used to relate the water level differences between several stations located on the same reservoir and the characteristics of the wind (direction and intensity). The study showed that the nonparametric regression model substantially improves the knowledge of the water level differences between several stations when there is presence of wind. With this model, it is possible to characterize the types of wind affecting the reservoir and to establish validation strategies for the data. The studied reservoirs are Outardes-4 and Gouin, two large reservoirs located in the north of the province of Québec, Canada.Key words: wind, reservoir, water level, nonparametric regression, natural inflow, performance criteria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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".