A Multi-Scale Intercepted Snow Sublimation Model
Bibliographic record
Abstract
Physically-based equations describing snow sublimation were used to provide a snow-covered forest boundary condition for a one-dimensional land surface scheme. The mass and energy balance equations were up-scaled from the snow grain scale to that of a canopy control volume using a fractal scaling technique. Modification of the land surface scheme's calculation of turbulent transfer and within-canopy ambient humidity were required to accommodate this nested control volume approach. Tests in late winter in a southern boreal forest mature jack pine stand against measured sublimation showed that the multi-scale model provides good approximations of sublimation losses during half-hourly periods and periods of 3 to 4 days. Sublimation averaged 0.5 kg m-2 daily, with minimum and maximum daily losses of 0.16 and 0.72 kg m-2 . Cumulative differences between estimates and measurements of canopy temperature, humidity, and intercepted snow load over 7 days of simulation were 0.7 K, 7.5 Pa, and 0.103 kg m-2, respectively. At a nearby regenerating jack pine site, measured peak latent heat flux density ranged from -14.6 W m-2 to -40.9 W m-2. Testing of the model at this site yielded reasonable estimates of latent and sensible heat fluxes during an overnight period, but did not estimate latent heat flux as well during periods involving larger snow loads and incoming solar radiation, possibly due to errors introduced by neglect of sub-canopy snow energetics. Further work to improve heat storage terms, and the inclusion of subcanopy snow energetics would improve the multi-scale model performance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".