USING A THERMAL IMAGER TO QUANTIFY BURIED THERMAL STRUCTURE IN NATURAL SNOW
Bibliographic record
Abstract
ABSTRACT: Avalanche researchers and practitioners have long measured snowpack temperatures in snow pits with thermometers about 10 cm apart. This led to the assumptions that temperature gradients are smooth and that temperature changes are regular. For this study, we used a thermal imager in standard snow pits in the Canadian Rocky Mountains during two seasons between 2010 and 2012. We collected the first season of data in a very shallow, below treeline snowpack study plot, and the second season of data in a deeper, treeline study plot. Data included thousands of thermal images, as well as visual macro images of the snow crystals in each pit layer to monitor changes. We observed strong temperature gradients on the scale of individual snow crystals. We found that these small scale gradients correlated with future snow crystal changes. We also found that these gradients changed quickly with the weather, even at depth. This paper focuses on our most recent findings from the 2011-12 season, and describes our overall progress in extracting data from thermal images to use for research and forecasting. We use correlations to present very general relationships between thermal data, crystal size, and layer stability tests. We also present temperature and gradient changes at depth during a period of clearing. 1.
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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".