Mountain Pine Beetles, Salvage Logging, and Hydrologic Change: Predicting Wet Ground Areas
Bibliographic record
Abstract
The mountain pine beetle epidemic in British Columbia has covered 18.1 million hectares of forest land showing the potential for exceptionally large-scale disturbance to influence watershed hydrology. Pine stands killed by the epidemic can experience reduced levels of evapotranspiration and precipitation interception, which can translate into an increase in soil moisture as observed by some forest practitioners during salvage logging in the epicenter of the outbreak. They reported the replacement of summer ground, dry firm soil areas, with winter ground areas identified by having wetter, less firm soils upon which forestry equipment operation is difficult or impossible before winter freeze-up. To decrease the likelihood of soil disturbance from harvesting, a set of hazard indicators was developed to predict wet ground areas in areas heavily infested by the mountain pine beetle. Hazard indicators were based on available GIS data, aerial photographs, and local knowledge. Indicators were selected by an iterative process that began with office-based selection of potential indicators, model development and prediction, field verification, and model refinement to select those indicators that explained most field data variability. Findings indicate that the most effective indicators were lodgepole pine content, understory, drainage density, soil texture, and the topographic index.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".