Bibliographic record
Abstract
ABSTRACT Mountain pine beetle (MPB) infestation and salvage logging create a post‐disturbance landscape composed of a patchwork of alive, dead, and clearcut forest stands. Subsequent impacts on runoff generation in snowmelt‐dominated hydrological regimes are largely a function of the proportion of landscape covered by each stand type, but basic information on snow accumulation variations between stand types following beetle infestation is lacking. This study examines snow accumulation in the post‐disturbance landscape of northern interior British Columbia, Canada, to quantify the effects of MPB‐related forest cover change. Field measurements collected in a live and a dead coniferous plot during the 2007 and 2008 winters are compared with those collected in a clearcut (canopy‐free control) to identify inter‐plot differences in snow depth, density, and water equivalent. Interannual variability in snow density and water equivalent is significantly affected by interannual variability in meteorological conditions. In high snow years, the dead and alive plots behave similarly due to the ability of large snowfalls to exceed the interception capacity of the canopy. In low‐to‐average snow years, however, distinct differences in snow accumulation between the dead and alive plots are observed. These are largely due to the canopy structure in the dead plot, which differs significantly from that in the healthy plot largely due to defoliation (needle drop) and the loss of fine branches and stems following beetle infestation. Research results provide key process information for modelling studies examining the effects of these changes on runoff generation at the watershed scale. Copyright © 2011 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 | 0.000 | 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) | 0.002 | 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".