Boundary‐layer integration approach to advection of sensible heat to a patchy snow cover
Bibliographic record
Abstract
Abstract In the calculation of the melting of a patchy snow cover, the energy advected from the adjacent bare soil to the snow surface is an important consideration. The quantity or rate of energy advected depends on the fetches and sizes of snow and bare ground patches. Any successful method to estimate advection will necessarily require the incorporation of relationships describing the same. Complex boundary‐layer methods require detailed spatial knowledge of the patch sizes, wind direction and fetch distances, and are computationally intensive. A physically based approach that can be spatially applied using distributions of snow patch geometry is required. This paper presents a new approach, in which boundary‐layer integration is used to provide a means of calculating the amount of energy removed by the snow patch surface as warmer air moves over it. The method is reduced to a simple parametric form, and the relationships describing the coefficients required for its application are developed. The applicability of this new approach is discussed in light of the fractal nature of snow patches, the relationship between the individual patch length and area, and the distribution of patch sizes as they develop and disappear on the landscape. Copyright © 2002 Crown in the right of Canada. Published by John Wiley & Sons Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".