Performance of Hot Plate for Measuring Solid Precipitation in Complex Terrain during the 2010 Vancouver Winter Olympics
Bibliographic record
Abstract
Abstract Solid precipitation intensity, snow density, wind speed, and temperature were collected from November 2009 to February 2010 at a naturally sheltered station located at an altitude of 1640 m MSL on Whistler Mountain in British Colombia, Canada. The snowfall was measured using the instruments OTT Pluvio; the Yankee Environmental Systems, Inc., hot plate (HP); and the Vaisala FD12P (optical weather sensor). The snow amount and density were also measured manually daily. The observed wind speeds were in the range 0–4.5 m s−1 with a mean value of 0.5 m s−1. Based on this study, the HP overestimated the snow amount by about a factor of 2 as compared to the Pluvio measurements. Further data analysis using the raw output HP data suggests that this was because of false precipitations produced, particularly by the downslope flows in the complex terrain when the wind speeds were relatively stronger. This false precipitation varied from −0.9 to 1.3 mm h−1 with two peaks at 0.1 and 0.3 mm h−1 depending on wind speed—the larger peak being at higher wind speeds. Since the observed wind speeds were relatively calm, setting the correction factor to 0.15 mm h−1 gave reasonable values as compared to the Pluvio data. The difference between the corrected HP and Pluvio accumulation data varied from 16% to 3% depending on wind speed. The observed snow density in January 2010 varied from 0.04 to 0.32 g cm−3 with a mean value of 0.08 g cm−3. The snow amount measured using the corrected HP data agreed well with the manually measured values with a correlation coefficient of 0.93.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".