A GIS‐based approach for areawide pest management: the scales of <i>Lygus hesperus</i> movements to cotton from alfalfa, weeds, and cotton
Bibliographic record
Abstract
Abstract Understanding the effect of cropping patterns on population dynamics, dispersal, and habitat selection of insect pests has been an unresolved challenge. Here, we studied the western tarnished plant bug, Lygus hesperus (Knight) (Heteroptera: Miridae), in cotton during early summer in central Arizona. We used a general approach based on global positioning system (GPS) and geographic information system (GIS) technologies combined with spatial statistics to assess the maximum distance at which forage and seed alfalfa, fallow fields with weeds, and cotton affect L. hesperus population density. Using a set of 50 cotton fields as focal fields, we found that forage and seed alfalfa as well as weeds acted as L. hesperus sources for these cotton fields. The source effect did not extend beyond 375, 500, and 1500 m for forage alfalfa, weeds, and seed alfalfa, respectively. Conversely, cotton fields acted as L. hesperus sinks, but this effect did not extend further than 750 m from the focal cotton fields. These findings suggest that specific spatial arrangements of these field types could reduce L. hesperus damage to cotton. The spatially explicit approach used here provides a direct evaluation of the effects of agroecosystem heterogeneity on pest population dynamics, dispersal, and habitat selection, which is a significant asset for the development and improvement of areawide pest management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".