Bibliographic record
Abstract
Abstract Accurately calculating the surface radiation budget of a groomed ski run is crucial when determining snow surface temperature and other snow-related variables, knowledge of which is important for ski racing. Downwelling longwave radiation can compose a large part of the surface radiation budget in mountainous terrain. At a location on a ski run, a portion of the downwelling longwave radiation comes from the sky and a portion comes from tall evergreen trees. Infrared photographs taken during daytime at a ski run on Whistler Mountain, British Columbia, Canada, for a clear-sky day in February 2012 show that trees can enhance the downwelling longwave radiation at the center of the ski run considerably, with a maximum estimated enhancement of 75.6 ± 16.8 W m−2 for trees in direct sunlight. The average needle and trunk brightness temperatures from the IR photographs were correlated with measured meteorological data. Regressions were found to allow estimation of longwave radiation from trees using nearby routine meteorological data. Absolute errors in tree longwave radiation estimations using the derived trunk and needle temperatures did not exceed 4 W m−2. The effect of the intervening air upon longwave radiative transfer between trees and the point of interest on the ski run was found to be small for these very short pathlengths of 50 m or less. These results can be used to improve calculations of the surface radiation budget of a groomed ski run under clear skies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".