Mountain pine beetle detection and monitoring: evaluation of airborne imagery
Bibliographic record
Abstract
The processing and evaluation of digital airborne imagery for detection, monitoring and modeling of mountain pine beetle (MPB) infestations is evaluated. The most efficient and reliable remote sensing strategy for identification and mapping of infestation stages ("current" to "red" to "grey" attack) of MPB in lodgepole pine forests is determined for the most practical and cost effective procedures. This research was planned to specifically enhance knowledge by determining the remote sensing imaging systems and analytical procedures that optimize resource management for this critical forest health problem. Within the context of this study, airborne remote sensing of forest environments for forest health determinations (MPB) is most suitably undertaken using multispectral digitally converted imagery (aerial photography) at scales of 1:8000 for early detection of current MPB attack and 1:16000 for mapping and sequential monitoring of red and grey attack. Digital conversion should be undertaken at 10 to 16 microns for B&W multispectral imagery and 16 to 24 microns for colour and colour infrared imagery. From an "operational" perspective, the use of twin mapping-cameras with colour and B&W or colour infrared film will provide the best approximation of multispectral digital imagery with near comparable performance in a competitive private sector context (open bidding).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".