Linking survey detection accuracy with ability to mitigate populations of mountain pine beetle
Bibliographic record
Abstract
In 2007, the mountain pine beetle impacted an estimated 10.1 million hectares of pine forest in British Columbia, Canada. Surveys to detect the location, size, and impact of infestations are conducted from field, airborne, and satellite perspectives. Importantly, the differing survey approaches characterize the infestation over dissimilar spatial scales (i.e., trees, stands, landscapes), and with varying levels of detection accuracy. In this communication, we provide background for understanding differing survey approaches, the nature of the information generated, the resultant detection accuracies that may be expected, and the link between survey accuracy and the ability to mitigate a given mountain pine beetle infestation. A detection accuracy of 100% implies that all infested trees could be mitigated; however, no survey method achieves this level of detection accuracy, and therefore some residual infestation will persist, facilitating further population expansion if other environmental factors are conducive. Based upon this understanding, we model the number of years of mitigation effort required to maintain endemic beetle population levels, as a function of the survey approach used and the expected detection accuracy. Key words: mountain pine beetle, survey, detection, remote sensing, accuracy, mitigation, insect, Landsat, QuickBird, aerial-overview survey, heli-GPS
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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 | 0.004 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".