A Sensitivity Study of Daytime Net Radiation during Snowmelt to Forest Canopy and Atmospheric Conditions
Bibliographic record
Abstract
This study investigates the dependence of net radiation at snow surfaces under forest canopies on the overlying canopy density. The daily sum of positive values of net radiation is used as an index of the snowmelt rate. Canopy cover is represented in terms of shortwave transmissivity and sky-view factor. The cases studied are a spruce forest in the Wolf Creek basin, Yukon Territory, Canada, and a pine forest near Fraser, Colorado. Of particular interest are the atmospheric conditions that favor an offset between shortwave energy attenuation and longwave irradiance enhancement by the canopy, such that net radiation does not decrease with increasing forest density. Such an offset is favored in dry climates and at high altitudes, where atmospheric emissivities are low, and in early spring when snow albedos are high and solar elevations are low. For low snow albedos, a steady decrease in snowmelt energy with increasing canopy cover is found, up to a forest density close to the actual densities of mature spruce forests. Snowmelt rates for high albedos are either insensitive or increase with increasing canopy cover. At both sites, foliage area indices close to 2 are associated with a minimum in net radiation, independent of snow albedo or cloud cover. However, these results are more uncertain for open forests because solar heating of trees may invalidate the longwave assumptions, increasing the longwave irradiance.
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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 | 0.000 | 0.000 |
| 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".