Evaluation and improvement of the PIEKTUK blowing snow model on the Canadian Prairies and Arctic
Bibliographic record
Abstract
Blowing snow is an impoftant part of Canadian's lives.Accurate forecasting of blowing snow events and their visibility can be an irnportant factor for the safety of Canadians.The PIEKTUK blowing snow model can be used to predict the occurrence of blowing snow, and to predict the visibility during an event.During an initial analysis, the model appeared to predict the occurrence of blowing snow very accurately, but once null weather events were renìoved, the model over-predicted more events than it correctly forecast.In an attempt to irnprove the forecasting capabilities of the model, the calculation for the threshold wind speed was tested by increasing and decreasing the constant coefficient in the equation incrementally to see if another value was optimal.For most stations, increasing the constant by 1 to 5 improved the forecasting of blowing sno\¡/ events over the original version.More consistent improvements were found in Prairie and Arctic regions than in Forest or Mountain regions.The influence of wind direction was also added into the model, and the results were analyzed for one Prairie station and one Arctic station.Minimal improvement was observed for Winnipeg, and none for Baker Lake.The predictions made by PIEKTUK were also compared to data recorded during the CASES project.Unlike earlier conclusions, decreasing the constant by -2 was found to improve the model the most due to the smooth nature of the surface (snow coveled first-year sea ice).. iii
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".