Snow surface energy exchanges and snowmelt in a shrub‐covered bog in eastern Ontario, Canada
Bibliographic record
Abstract
Abstract The objectives of this study were to measure and evaluate the energy balance of a snowpack in a northern peatland, with a particular emphasis on the ground heat flux (G), and to evaluate the performance of a point energy and mass balance snowmelt model (SNOBAL) in peatland ecosystems. G is typically considered a small component of the snowpack energy balance (EB) when compared with radiative and turbulent fluxes. However, in environments where the soil temperature remains above freezing throughout the winter, G may be an important energy input to the snowpack. For direct assessment of the role of G in the snow energy budget of such an environment, the EB components of the snowpack at the Mer Bleue bog, a northern peatland, were directly measured and modelled using SNOBAL during the 2009–2010 winter. When integrated over the pre‐melt period, simulated and measured G proved to be a large contributor to the EB (25%). Net radiation and G were somewhat under‐predicted by SNOBAL, whereas turbulent fluxes (especially latent heat fluxes LE) were considerably over‐predicted. G calculated by SNOBAL was found to be sensitive to the temperature gradient between the soil and the lower layer of the snowpack, whereas simulated turbulent fluxes were sensitive to the parameterization chosen to estimate roughness lengths for heat and water vapour. Copyright © 2012 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".