Development and Application of K-Nearest Neighbour Weather Generating Model
Bibliographic record
Abstract
A generic weather generator, based on the K-nearest neighbour algorithm, is presented for producing synthetic weather sequences that can be used in conjunction with hydrological models. Application of the model to the Upper Thames River basin in Ontario has clearly demonstrated the practicality of the approach in generating plausible climate change scenarios for the basin. Daily weather variables (maximum temperature, minimum temperature, and precipitation) were simulated at multiple stations in the basin. Statistical analysis of the synthetic series generated by the model clearly demonstrated the ability of the model to reproduce important statistical parameters of the observed data series such as the mean, variance, and skewness. A distinct practical advantage of the approach presented here over the traditional Richardson and serial type weather generator is that the spatial correlation of the variables can be adequately reproduced. Cross-correlations of the variables at a station, and autocorrelations of variables, are also strongly preserved. Site-specific assumptions regarding the probability distributions of the variables are not required thereby permitting transportability of the model to other basins with very few modifications.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".