Simulation of Snowmelt in a Subarctic Spruce Woodland: Scale Considerations
Bibliographic record
Abstract
Subarctic woodlands comprise stands of spruce trees with varying degrees of openness, giving rise to large contrasts in melt rates within the forest. The spatial variability of the changing snow depth during a melt season was investigated at three scales (2,4 and 16 m), using an example from a site in Yukon, Canada, where the computation of snowmelt takes into account the differential rates within the woodland. During the melt period, the mean daily snow depth decreases but the variability increases as continued ablation leads to greater unevenness of the snow cover. At the three scales of representation, increasing the grid size results in a reduction in the standard deviation and the skewness of depth distribution. The blurring of snow cover pattern at the larger scales is due to a loss in information, considered as the absolute value of the difference in snow depth calculated at two scales for the same location. This loss increases as the snow depth becomes more variable during the melt season. Knowledge of the scale-induced information loss is relevant to the modelling of snowmelt that exhibits large spatial variations.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".