A correction for serial nonindependence in mountain pine beetle aerial survey data to reduce overestimation of cumulative damage
Bibliographic record
Abstract
At the beginning of this century, an unprecedented mountain pine beetle ( Dendroctonus ponderosae Hopkins) outbreak expanded across western North America. Methods are needed to quantify its impact on forests. Although aerial survey data of infestation are readily available, their translation to tree mortality estimates is challenging because the polygons sketched by aerial surveyors to delimit infested regions often overlap from one year to the next, which produces a lack of independence in yearly estimates of infested landscape. This problem manifests when annual proportions of infested land are summed over years because the sum often exceeds one. Cumulative proportions of infested land larger than one implies overestimation of tree mortality if initial densities of available host trees are multiplied by annual proportions of infested landscape. To address this problem, we developed a probabilistic method for correcting for nonindependence in proportions of infested land recorded using overlapping polygons. We demonstrate our approach in Jasper National Park where the probabilistic correction reduces overestimation bias in regions that were infested multiple years when predictions are compared to validation data. The approach and the spatial raster data sets we generated will be useful to forest managers seeking to quantify the impact of mountain pine beetles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".