A Survey of Regional Trends in Annual Bluegrass Weevil (Coleoptera: Curculionidae) Management on Golf Courses in Eastern North America
Bibliographic record
Abstract
The annual bluegrass weevil, Listronotus maculicollis Kirby, is the most difficult to control insect pest of short-mown golf course turf in the northeastern United States and Eastern Canada. We conducted a survey among golf course superintendents throughout the weevil's area of impact to better understand the severity of damage, prevalence of insecticide resistance, information sources, and trends in management practices. Responses were received from 293 golf courses in 14 U.S. states and 2 Canadian provinces. The average population caused damage to 6.6 fairways, 5.7 tee boxes, and 6.4 greens/collars, amounting to a total of 5.2 ha requiring protection on an 18-hole facility. On average, courses made 3.9 insecticide applications per year and spent US$9,270 on L. maculicollis management. Twenty percent of the responders reported having a pyrethroid-resistant L. maculicollis population. “Resistant” populations were located across the region, though higher-than-average incidence was reported from areas with long histories of managing L. maculicollis. “Resistant” populations caused more damage than “susceptible” populations, reported higher average insecticide budgets, and were more likely to make more than five insecticide applications per year than “susceptible” courses. Surveys indicated that, despite the reliance on chemical controls, 90% of turf managers used multiple monitoring tactics to better time and target controls. The greatest influence on management philosophy was by University personnel (43%) followed by colleagues (31%) and sales/distributors (21%). This survey highlights the need for developing alternatives to chemical insecticides to control L. maculicollis and provides insight into the costs associated with the development of pyrethroid resistance.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".