Clearcutting Impacts on Snow Accumulation and Melt in a Northern Hardwood Forest
Bibliographic record
Abstract
Snow accumulation and melt on north- and south-facing slopes in the Turkey Lakes Watershed (TLW) in central Ontario were compared for a mature hardwood maple stand and an adjacent clearcut. Snow accumulation in the clearcut exceeded that in the forest. Melt was significantly larger in the south-facing clearcut and forest relative to corresponding north-facing sites. Daily melt in the clearcut on the north-facing slope was slightly greater and more spatially variable than in the adjacent forest. Nevertheless, the control of aspect on the spatial variations in melt was larger than that due to clearcutting. Micro-scale variations in canopy density did not explain inter-point differences in daily melt within either the clearcut or the forest. The hydrological consequences of greater pre-melt snow water equivalent and larger daily melt in clearcuts include quicker delivery of meltwater to the soil surface and promotion of rapid near-surface runoff to receiving waters relative to undisturbed forest stands at TLW.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".