Twenty-Seven Years of Manual Fresh Snowfall Density Measurements on Whistler Mountain, British Columbia
Bibliographic record
Abstract
Snow density is important information for a wide range of activities including avalanche control, marketing, building-code development, weather forecasting, and water supply forecasting. Extended recent high-quality datasets from the mountainous regions of the Pacific Northwest coastal area are rare. This paper presents a study of an unusually long and continuous (January 1990 to April 2016) manually collected dataset of fresh snowfall measurements for Whistler Mountain, British Columbia, Canada. The dataset consists of snowboard core measurements that were collected by Whistler–Blackcomb ski patrol staff twice daily for avalanche control and resort-marketing purposes. These records were collated, transcribed, quality controlled, and made computer accessible in this study. A discussion of the characteristics of the data collection site and an assessment of data reliability are presented. Two examples of the many purposes to which this high-quality dataset might be put were studied. Climatic teleconnections to winter (December–February) mean snow density were examined, which revealed a positive relationship to the quadratic form of the Pacific Decadal Oscillation pattern (i.e., PDO2). In addition, an analysis of daily snow density relationships to air mass types was performed, which suggested that higher (lower) densities are associated with maritime inflow (arctic outflow) conditions. Both of these relationships appear to be mediated by the positive correlation between snow density and air temperature.Based on the full dataset (N = 1275 individual snow density measurements) for all months with measured snowfall, annual snowfall season (November to May) mean snow densities ranged from 77 kg m−3 to 109 kg m−3 with an overall mean of 91 kg m−3, giving an overall snow-depth to water-depth ratio of 11:1.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".