Optimizing over-summer snow storage at low latitudes and low altitudes
Bibliographic record
Abstract
Climate change is forcing the ski industry to modify snow-making strategies and facility operations. Over-summer snow storage is an adaptation successfully employed by high-elevation and/or high-latitude ski centers in Europe, Canada, and Asia. The process involves stockpiling winter snow and storing it beneath insulation (e.g., wood chips) through summer. Current methods are empirically-based with few studies quantifying snowmelt through summer or comparing insulation strategies. In this project, we evaluate the feasibility of over-summer snow storage in Vermont, northeastern North America. Soil temperatures were recorded since June 2017 with sensors 5, 20, 50 cm and 1 m below the ground surface. In March 2018, two, 200 m3 snow piles were covered in plastic and wood chips; we monitored their volume bi-weekly through the melt season using terrestrial LiDAR. We also measured air to snow temperature gradients under various insulation materials: rigid foam, open cell foam, and wet wood chips, all with and without reflective coverings. Away from snow piles, ground temperatures at 1 m depth were ~7C in spring 2017, rising to 12C in summer, and falling to just above 0C in winter. As depth decreased, ground temperature became more responsive to air temperature; ground temperature lagged air temperature at all depths. Below summer snow piles, soil temperature at all depths remained near freezing through the summer as cold meltwater percolated into the ground. Snow was lost from each pile at a similar rate (~1.3 m3 day-1) from late March to mid-June; melt then accelerated slightly in response to increased air temperature, solar radiation, and humidity. Large crevasses formed in both piles along the edge of the plastic sheeting which exposed snow to direct sunlight. Temperature was at or above 10C over the snow below both rigid foam and open-cell foam with a strong diurnal variation, regardless of the addition of a reflective blanket. Beneath wet wood chips covered with a reflective blanket, temperature remained close to freezing even though air temperature was > 30C. There was no diurnal variation, indicating that wood chips effectively buffered thermal swings. It appears that a reflective surface over >20cm of wet wood chips is most effective at minimizing summer snow melt in humid, northeastern North America.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".