Mountain Pine Beetle: How Forest Mismanagement and a Flawed Regulatory Structure Contributed to an Uncontrollable Epidemic, The
Bibliographic record
Abstract
During the late 1990s, a tiny insect began ravaging the forests of the Rocky Mountain West, leaving in its wake millions of acres of ghost forests 1 and presenting forest managers and policy makers with the unenviable task of mitigating one of the greatest impacts to western forests in recorded history.The mountain pine beetle is endemic to lodgepole pine dominated forests of the western United States and Canada.For generations, the beetle has quietly killed mature trees and left their remains 2 to provide valuable nesting habitat and food sources for birds and small mammals and a means for younger trees to establish themselves.3 * Mr. Willms is an Assistant Attorney General with the Water and Natural Resources Division of the Wyoming Attorney General's Office.Mr. Willms received his education from the University of Wyoming, earning a B.S. in Wildlife and Fisheries Biology and Management as well as Environment and Natural Resources in 2002 and a J.D. from the College of Law in 2005.The opinions expressed here are solely those of the author and not those of the Attorney General's Office
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".