Optimal planning of multi‐day invasive species surveillance campaigns
Bibliographic record
Abstract
Abstract Multi‐day survey campaigns are critical for timely detection of biological invasions. We propose a new modelling approach that helps allocate survey inspections in a multi‐day campaign aimed at detecting the presence of an invasive organism. We adopt a team orienteering problem to plan daily inspections and use an acceptance sampling approach to find an optimal surveillance strategy for emerald ash borer in Winnipeg, Manitoba, Canada. The manager's problem is to select daily routes and determine the optimal number of host trees to inspect with a particular inspection method in each survey location, subject to upper bounds on the survey budget, daily inspection time, and total survey time span. We compare optimal survey strategies computed with two different management objectives. The first problem minimizes the expected number of survey sites (or area) with undetected infestations. The second problem minimizes slippage – the expected number of undetected infested trees in sites that were not surveyed or where the surveys did not find infestation. We also explore the impact of uncertainty about site infestation rates and detection probabilities on the surveillance strategy. Accounting for uncertainty helps address temporal and spatial variation in infestation rates and yields a more robust surveillance strategy. The approach is generalizable and can support delimiting survey programs for biological invasions at various spatial scales.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".