Verification of ESP forecast skills for pre- and post-ESP re-sampling schemes: Application to the South Saskatchewan River Basin
Bibliographic record
Abstract
This study compares the performance of two K-nearest neighbor (K-NN) re-sampling schemes for producing ensemble streamflow forecasts using a conceptual hydrologic model. In the first scheme, the weather input data to the hydrologic model (precipitation and temperature) for each day of the forecast year are stochastically generated from historical observations by conditioned re-sampling from the K-NN. In the second scheme, ensemble members are conditionally re-sampled from candidate ensemble traces which were generated by assuming that each historical year in the record has an equal likelihood of occurrence in the forecast year. In both schemes, the conditioning vectors for selecting the nearest neighbors comprise large-scale climate information and antecedent precipitation. The methods were applied to two watersheds located in the headwaters of the South Saskatchewan River basin in the province of Alberta, Canada. Forecasts produced by the two schemes exhibited only marginal differences in terms of overall skill measures such as correlation coefficient, relative root-mean-squared error and ranked probability skill score. However, notable differences were observed between forecasts issued during some months when the relative operating characteristic curve was evaluated for below-normal and above-normal flow categories separately.
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 | 0.004 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".