Measuring Near-Surface Snow Temperature Changes Over Terrain
Bibliographic record
Abstract
The energy balance at the snow surface results in substantial diurnal temperature fluctuations within the top portion of the snowpack. These temperature fluctuations, which vary both spatially and over time, can have important effects on stability. During the winters of 2005 and 2006, field data for a near-surface warming study were collected on a treeline knoll located in the Columbia Mountains of British Columbia. Thermocouple arrays placed on the top and at different locations on the knoll side slopes recorded temperatures within the top 30 cm of the snowpack. During each test period, nearby measurements of incoming short and long wave radiation were collected, as were periodic observations of cloud cover, air temperature and the snow surface temperature at each array. The study aims to use field data to identify when, and to what extent, near-surface warming occurs on different aspects. Instrumentation challenges encountered in accurately measuring near-surface snow temperatures are also presented. Preliminary analysis of the data shows a significant correlation between aspect and the maximum daytime increase in near-surface snow temperatures. In one experiment, the magnitude of daytime warming measured at 10 cm depth was almost 7 oC greater on the south aspect than the north aspect. The aspect dependent differences in daily temperature change decreased with increased cloud cover.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 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".