Use of a thermal imager for snow pit temperatures
Bibliographic record
Abstract
Abstract. Weak snow of interest to avalanche forecasting often forms and changes as thin layers. Thermometers, the current field technology for measuring the temperature gradients across such layers – and for thus estimating the expected vapour flux and future type of crystal metamorphism – are difficult to use at distances shorter than 1 cm. In contrast, a thermal imager can provide thousands of simultaneous temperature measurements across small distances with better accuracy. However, a thermal imager only senses the exposed surface, complicating its methods for access and accuracy of buried temperatures. This paper presents methods for exposing buried layers on pit walls and using a thermal imager to measure temperatures on these walls, correct for lens effects with snow, adjust temperature gradients, adjust time exposed, and calculate temperature gradients over millimetre distances. We find lens error on temperature gradients to be on the order of 0.03 °C between image centre and corners. We find temperature gradient change over time to usually decrease – as expected with atmospheric equalization as a strong effect. Case studies including thermal images and visual macro photographs of crystals, collected during the 2010–2011 winter, demonstrate large temperature differences over millimetre-scale distances that are consistent with observed kinetic metamorphism. Further study is needed to use absolute temperatures independently of supporting gradient data.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 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".