A.: Small-scale variation in snowmelt energy in a boreal forest: An additional factor controlling depletion of snow cover
Bibliographic record
Abstract
Snow ablation and snow cover depletion beneath a forest canopy were investigated at the fine to stand scale, first by theoretical considerations and modelling and secondly using fine-scale measurements of changes to snow water equivalent (SWE) as an indicator of melt energy and of ablation. Three primary differences between observed areal snow ablation and snow cover depletion and calculations that presume uniform snow and energy were investigated: 1) spatial variation in initial snow mass, 2) spatial variation in melt energy, 3) spatial covariance between melt energy and initial snow mass. A theoretical analysis showed that all three effects can result in areal ablation rates being smaller than available melt energy and contribute effects that cause snow cover depletion curves similar to those that are frequently observed. Field data from a dense boreal spruce stand in the Yukon Territory showed that the spatial distribution of snow water equivalent was lognormally distributed and independent of the energy available for melt, which was normally distributed. Though the spatial variation in melt energy was significant in this forest, ablation calculations that used a synthetic distribution of snow water equivalent and presumed uniform melt energy were sufficient to describe snow cover depletion rates and snow ablation.
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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".