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 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 | 0.101 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".