Influence de la taille des régions homogènes sur la qualité de l'ajustement des crues de rivières non jaugées du Québec
Bibliographic record
Abstract
The influence of the size of homogeneous regions on the goodness of fit of ungauged river floods is studied by cross validation. Two initial regions, one homogeneous and the other potentially homogeneous, formed by 38 and 34 rivers were used. Homogeneous sub-regions of various sizes were randomly created to study the behaviour of the non-selected rivers, considered as ungauged for the purpose of this study. Results have shown that the size of the sub-regions has less impact on the 2 test results than the inherent quality of each river. In fact, the size of the sub-regions was inversely proportional to the variability, which means that a region of small size has a larger chance to lead to realisation exceeding the χ2 test critical value than a region of large size. In spite of this finding, the influence of the size of the regions was small if one considers that for the worst case scenario (homogeneous sub-regions of five rivers), the percentage of failure of the χ2 test was increased by only about 3%. However, the distribution of the regional L-moment ratios decreases with the size of the sub-regions. The selection of larger homogeneous regions thus allows a reduction in the variability of the estimation of regional T-year events.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".