Forecasting Sun versus Shade in Complex Terrain for the 2010 Winter Olympic and Paralympic Games
Bibliographic record
Abstract
During the 2010 Vancouver Winter Olympic and Paralympic Games in Canada, there were 10 mostly sunny days at the outdoor Olympic venues. The warmth and sunshine, possibly a result of El Niño conditions at the time, significantly reduced snow cover at one venue and weakened the snowpack at the other two venues, much to the chagrin of the event organizers. Solar radiation affects ski racing via its effect on snow-surface friction, abrasion, and mechanical strength. Ski technicians and athletes compensate via the choice of ski and wax. For these reasons, sun-versus-shade forecasts were produced for Canadian ski and snowboard teams. A theodolite was used to survey the horizon elevation angles around the full azimuth circles at 133 locations spaced roughly 150 m apart along race pistes (compacted ski runs) at three Olympic venues. This survey was important for including the shadowing effects of the tall evergreen trees that border the pistes. This would not be properly accounted for if only digital elevation data were used. These data, along with the astronomical equations for solar elevation and azimuth, were used to calculate whether each survey point would be in the sun or the shade in cloudless conditions for any time and date during the Olympics. Half-hourly output was provided to ski and snowboard technicians and coaches via a graphical user interface delivered on the Internet.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".