Gene Drives Across Engineered Fitness Valleys: Modeling a Design to Prevent Drive Spillover
Bibliographic record
Abstract
1 Abstract Engineered gene drive techniques for population replacement and/or suppression have potential for tackling complex challenges, including reducing the spread of diseases and invasive species. Unfortunately, the self-propelled behavior of drives can lead to the spread of transgenic elements beyond the target population, which is concerning. Gene drive systems with a low threshold frequency for invasion, such as homing-based gene drive systems, require initially few transgenic individuals to spread and are therefore easy to implement. However their ease of spread presents a double-edged sword; their low threshold makes these drives much more susceptible to spread outside of the target population (spillover). We model a proposed drive system that transitions in time from a low threshold drive system (homing-based gene drive) to a high threshold drive system (underdominance) using daisy chain technology. This combination leads to a spatially restricted drive strategy, while maintaining an attainable release threshold. We develop and analyze a discrete-time model as proof of concept and find that this technique effectively generates stable local population suppression, while preventing the spread of transgenic elements beyond the target population under biologically realistic parameters.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".