Estimates of bark beetle infestation expansion factors with adaptive cluster sampling
Bibliographic record
Abstract
Insects have infested over 37 million hectares of forested land, the most aggressive forest insect pest in North America is the mountain pine beetle that has attacked 14 million hectares. To determine infestation extent and spread rates, we examined mountain pine beetle damage at two sites over two consecutive years (2007–2008). High spatial resolution (20 cm) airborne digital imagery was acquired over a range of infestation intensities (High: site A; Low: site B). An adaptive cluster sampling approach assessed the extent and severity of damage from the imagery. In 2007, site A contained 5.22 infested trees per hectare (variance: 10.65) increasing in 2008 to 11.02 trees per hectare (variance: 24.83). In contrast, site B had 0.25 infested trees per hectare in 2007 (variance: 0.02), which increased in 2008 to 0.47 trees per hectare, with a variance of 0.08 trees per hectare. At both sites, infestations approximately doubled over a 1-year period. Adaptive cluster sampling applied to high spatial resolution airborne imagery can provide estimates of the severity of attack on the landscape.
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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 | 0.008 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".