Invasive forest pest surveillance: survey development and reliability
Bibliographic record
Abstract
Worldwide, a large number of potential pest species are introduced to locations outside their native ranges; under the best possible prevention scheme, some are likely to establish one or more localized populations. A comprehensive early detection and rapid-response protocol calls for surveillance to determine if a pest has invaded additional locations outside its original area of introduction. In this manuscript, we adapt and spatially extend a two-stage sampling technique to determine the required sample size to substantiate freedom from an invasive pest with a known level of certainty. The technique, derived from methods for sampling livestock herds for disease presence, accounts for the fact that pest activity may be low at a coarse spatial scale (i.e., among forested landscapes) but high at a fine scale (i.e., within a given forested landscape). We illustrate the utility of the approach by generating a national-scale survey based on a risk map for a hypothetical forest pest species threatening the United States. These techniques provide a repeatable, cost-effective, practical framework for developing broad-scale surveys to substantiate freedom from non-native invasive forest pests with known statistical power.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".