NEAR INFRARED PHOTOGRAPHY TO QUANTIFY TEMPORAL CHANGES IN MELT-FREE ZE CRUSTS
Bibliographic record
Abstract
Objective quantification of snowpack stratigraphy is a difficult task, especially when multiple ob- servers may be tracking changes over time. Properties such as grain type and size, though well defined by various snow observation guidelines, are often interpreted differently. One potential solution is the use of near infrared (NIR) photography to track the specific surface area (SSA) of a given layer. Processed images provide a quantitative measure of in-situ grain morphology free of any requirements for interpretation in the field. For the past two winters the Applied Snow and Avalanche Research Group at the University of Calgary has used NIR photography to track changes in and around buried melt-freeze crusts in the Columbia Mountains of western Canada. Eight crusts were tracked using both NIR and manual observations for periods ranging from 5 to 12 weeks. This paper describes the methods used and presents results from this study. Advantages of the method over traditional observations are also discussed, as are challenges encountered over the past two seasons.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".