Precipitation Simulation Based on k-Nearest Neighbor Approach Using Gamma Kernel
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a weather generator that produces new values of precipitation to generate realistic weather sequences. The model has been applied to a network of 14 meteorological stations around the Upper Thames River Basin (UTRB), Ontario, Canada. We developed a simple model that employs the k-nearest neighbor resampling approach with gamma kernel perturbation. This gamma kernel perturbation enables the production of new values rather than merely reshuffling the historical data to generate realistic weather sequences. Daily precipitation was simulated at all the locations in and around the considered basin. The comparison of simulated data to the observed data led to the conclusion that the proposed perturbation algorithm performs quite well at preserving the monthly and annual historical statistics. The improved model was shown to produce precipitation amounts different from those observed in the past record.
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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 | 0.001 | 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 it