INTEGER PROGRAMMING APPROACH TO CONTROL INVASIVE SPECIES SPREAD BASED ON CELLULAR AUTOMATON MODEL
Bibliographic record
Abstract
Abstract We propose a new optimization model that captures the spatial dynamics of invaders by a cellular automaton model and finds the optimal solution to control its spread within a 0–1 integer programming framework. The model seeks a solution by minimizing the total costs to implement treatments for preventing the spread and damage caused by invaders’ colonization. By incorporating a cellular automaton model governed by state‐ and distance‐dependent probability rule of colonization, the model is transformed into a linear model, so that a 0–1 integer programming formulation is used to evaluate and compare an optimal allocation of treatments on colonized and uncolonized areas. The study uses a hypothetical map to show that treatments on colonized cells are more effective when implemented at the front line of the invaders, while treatments on uncolonized areas are effective when conducted with some distance or buffer zone away from the front line. These buffer zones are likely to be colonized regardless of treatment. Under annual budget limits, treatments on colonized cells are implemented first. With heterogeneity in the invaders’ dynamics, the proposed optimization model provides an optimal allocation of treatments much different from the solution with homogeneous environment. However, treatment at the front line of the invading species is always recommended.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".