Getting at the Root of the Mountain Pine Beetle's Rapid Habitat Expansion and Forest Devastation
Bibliographic record
Abstract
The mountain pine beetle has wreaked havoc in North America, across forests from the American Southwest to British Columbia and Alberta, with the potential to spread all the way to the Atlantic coast. Millions of acres of forest have been lost, with severe economic and ecological impacts from a beetle outbreak ten times larger than previous outbreaks. Because of its importance and impact on forestry, the mountain pine beetle's genome has been recently sequenced. Using this new resource, authors Janes et al. (2014) examined how the pine beetle could undergo such rapid habitat range expansion and whether population genetics and the cataloguing of genome wide mutations could shed any light on possible molecular causes of the outbreak. From beetles collected at 27 sites in Alberta and British Columbia, they looked for any patterns among their catalog of 1,536 mutations (single-nucleotide polymorphisms). They found several candidate genetic markers and conclude that the mountain pine beetle may have been able to spread by adjusting its cellular and metabolic functions to better withstand cooler climates and facilitate a larger geographic dispersal area. Such information could give important new clues for the forestry industry to help curb the current devastation of North American forests from this pest.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".