Optimal planning of multi‐day invasive species surveillance campaigns
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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 it