Spatial distribution of surface hoar crystals in sparse forests
Bibliographic record
Abstract
Abstract. Surface hoar size and location relate directly to avalanche initiation trigger points, and they do so in small-scale spatial distributions. Physically, surface hoar will grow where the snow surface is cold relative to the air and water vapour is plentiful. Vapour aside, snow cools at night primarily by longwave radiation emittance. Emittance can be restricted by clouds, trees, and terrain features. With 96 independent spatial point samples of surface hoar size, we show the extreme small-scale size variation that trees can create, ranging from 0 to 14 mm in an area of 402 m2. We relate this size variation to the effects of trees by using satellite photography to estimate the amount that trees impinge on sky view for each point. Though physically related to longwave escape, radiation balance can be as difficult to estimate as surface hoar size itself. Thus, we estimate point surface hoar size by expected maximum areal crystal size and dry terrain greyscale value only. We confirm this relation by using it at a different area and in a different formation cycle. There, its overall average error was 1.5 mm for an area with surface hoar sizes ranging from 0 to 7 mm.
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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 | 0.000 |
| 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".