An Unusual Cold February 2019 in Saskatchewan—A Case Study Using NCEP Reanalysis Datasets
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
In February 2019, central Canada, and especially the province of Saskatchewan, experienced extreme cold weather. It was the coldest February in 82 years and the second coldest in 115 years. In this study, we examine National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) Reanalysis 1 data to understand the atmospheric processes leading to this cold snap. A detailed investigation of surface air temperature, sea level pressure, surface fluxes, and winds revealed a linkage between the North Pacific storm track and the February cold snap. A shift in the jet stream pattern triggered by the storm activity over the North Pacific caused a high-pressure blocking pattern, which resulted in unusual cold temperatures in Saskatchewan in February. This study demonstrates the potential for extreme cold in a warming climate; weather records in Saskatchewan show an increase in minimum winter temperature by 4–5 °C.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it