Optimal refuge strategies to fight pest resistance to GM crops
Bibliographic record
Abstract
We consider the use of a genetically modi ed crop to ght a pest population that feeds on the crop. We use an entomological model that captures the diversity of the pest population's gene pool, as well as the level of pest invasion itself. A refuge area is used as an instrument to control the evolution of the susceptibility of the pest's gene pool to the genetically modi ed crop. We characterize the refuge area that minimizes the sum of discounted costs related to the crop damage caused by the pest as well as the supplemental cost of the genetically modi ed crop. The model is calibrated for the use of Bt-corn to ght the European corn borer. Because of the linearity of the objective function, the optimal refuge consists of a bang-bang and a singular control. For the calibrated parameters, as well as reasonable variations of them, the bio-economic system tends to an interior steady state where the level of pest-susceptibility is renewable.However, when the control is restricted to being constant over time, as is currently done in the United States, the system generally tends to a steady state where the susceptibility is completely exhausted. In that case, it takes very particular parameter constellations for the system to reach an interior steady state. We are able to assess,for the calibrated model, the cost reduction attained by using a refuge area that varies over time instead of a time-invariant one.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.004 | 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".