Optimizing the location of watercraft inspection stations to slow the spread of aquatic invasive species
Bibliographic record
Abstract
Abstract The accidental spread of aquatic invasive species (AIS) by recreational boaters is a major concern of state and county environmental planners in the USA. While programs for watercraft inspection to educate boaters and slow AIS spread are common practice, large numbers of boats and waterbodies, together with limited budgets, make program design difficult. To facilitate program design, we developed an integer programming model for allocation of scarce inspection resources among lakes. Our model uses species-specific infestation status of lakes and estimates of boat movement between lakes. The objective is to select lakes for inspection stations to maximize the number of risky boats inspected, where risky boats are ones that move from infested to uninfested lakes. We apply our model in Stearns County in central Minnesota, USA, to prioritize lakes for inspection stations and evaluate alternative management objectives. With an objective of protecting uninfested lakes within and outside Stearns County, the optimal policy is to locate stations at infested lakes having the most boats departing for uninfested lakes inside and outside the county. With an objective of protecting only Stearns County lakes, the optimal policy is to locate stations at both infested and uninfested lakes having the riskiest boats arriving from within and outside the county and departing to in-county lakes. The tradeoff between these objectives is significant.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".