Improved K-Nearest Neighbor Weather Generating Model
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
A major limitation of K -nearest neighbor based weather generators is that they do not produce new values but merely reshuffle the historical data to generate realistic weather sequences. In this paper, a modified approach is developed that allows nearest neighbor resampling with perturbation of the historic data. A strategy is introduced that resamples the historical data with perturbations while preserving the prominent statistical characteristics, including the interstation correlations. The approach is similar in spirit to traditional autoregressive models except that the new values are obtained by adding a random component to the individual resampled data points. An advantage of the approach is that unprecedented precipitation amounts are generated that are important for the simulation of extreme events. The approach is demonstrated through application to the Upper Thames River Basin in Ontario. Daily weather variables (maximum temperature, minimum temperature, and precipitation) were simulated at multiple stations in and around the basin. Analysis of the simulated data demonstrated the ability of the model to reproduce important statistical parameters of the observed data series while allowing perturbations to the observed data points. Additionally, no site-specific assumptions regarding the probability distribution of variables are required.
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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