Classification of Clustered Snow Off Dates Over British Columbia, Canada, from Mean Sea Level Pressure
Bibliographic record
Abstract
Atmosphere–ocean teleconnections influence the accumulation and melt of snow in western Canada and can be useful in seasonal forecasting of snowmelt and runoff. Interannual variation in these atmosphere–ocean modes has been shown to influence the accumulation and melt of snow within British Columbia (BC), Canada. We investigate fall mean sea level pressure (MSLP) globally as a predictor of remotely sensed snowmelt dates within BC. We use the last day of continuous snow cover (SDoff) detected from time series satellite imagery acquired by the Moderate Resolution Imaging Spectroradiometer for the hydrological years 2000–2018. It has been shown that SDoff is correlated with continuous snow duration and is also of interest to seasonal forecasters. Global MSLP from the Fifth major global reanalysis produced by the European Centre for Medium-range Weather Forecasts was obtained over hydrological years 1979–2018. An S-mode (time versus location) principal component analysis was carried out on both datasets. The SDoff principal component scores were grouped using a k-means clustering routine. Using evolutionary feature selection, the subset of MSLP principal components that provided good linear discrimination of the SDoff clusters were found. We explore the atmospheric MSLP principal components that influence the timing of snowmelt over BC and use them to predict the SDoff clusters at a seasonal lead time.
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How this classification was reachedexpand
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) | 0.008 | 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 teacher head, 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".