To Ski or Not to Ski: Estimating Transition Matrices to Predict Tomorrow's Snowfall Using Real Data
Why this work is in the frame
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Bibliographic record
Abstract
Using historical data from the Global Historical Climatology Network (GHCN)-Daily database, the use of Markov chain models is presented to predict a ‘Snow Day’ at eight national weather stations. This serves as a variation of the classic Markov chain precipitation example, predicting a significant snow depth tomorrow from today's snow depth conditions. Stations near Seattle WA, Denver CO, Milwaukee WI, Chicago IL, New York NY and Boston MA, were included as they represent major urban centers, while stations in Montana and North Dakota were added to improve geographical coverage. Estimates of the appropriate transition matrices (ˆi) are provided, as well as a sample of code in the R statistical programming language to enable construction of similar examples for other geographical areas.
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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 | 0.001 |
| 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) | 0.001 | 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