Optimal allocation of invasive species surveillance with the maximum expected coverage concept
Bibliographic record
Abstract
Abstract Aim We address the problem of geographically allocating scarce survey resources to detect pests in their pathways of introduction given information about their likelihood of movement between origins and destinations. We introduce a model for selecting destination sites for survey that departs from the aim of reducing propagule pressure (PP) in pest destinations and instead aims to increase monitoring of pest origins. The model is a maximum expected coverage problem (MECP), which maximizes the expected number of origins that are covered by the survey system, where an origin is covered if at least one of its transmission pathways connects to a surveyed destination. For comparison, we present two models that aim to reduce PP in destination sites. One model maximizes the expected number of transmission pathways that are covered by survey locations and the other maximizes the expected number of survey locations that have one or more pest introductions. Location United States, Canada. Methods We demonstrate the models by analysing the human‐mediated spread of the emerald ash borer (Agrilus planipennisFairmaire), a major pest of ash trees in North America, by visitors to campgrounds in central Canada and the US Midwest. The models incorporate estimates of spread rates from a network of campers travelling from approximately 6500 invaded domains to 266 uninvaded campgrounds in three Canadian provinces (Ontario, Quebec and Manitoba) and three US states (Michigan, Minnesota and Wisconsin). Results The MECP and PP‐based model solutions agreed for large surveillance budgets but exhibited differences when the budgets were small. These results stem from differences between the coverage‐based objective in MECP and the PP‐based metrics in the PP models. Main conclusions Our comparison of MECP and PP‐based models reveals the trade‐offs between objectives. Overall, the MECP is generic and can be adapted to survey species that are spread via other human‐mediated vectors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".