Verification of ESP forecast skills for pre- and post-ESP re-sampling schemes: Application to the South Saskatchewan River Basin
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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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 | 0.000 | 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 it