Lawn Parameters Influencing Abundance and Distribution of the Hairy Chinch Bug (Hemiptera: Lygaeidae)
Bibliographic record
Abstract
Management of lawns that promotes conditions detrimental to the development of insect pests may represent a valuable environmentally benign turfgrass management strategy. In the cool-humid region of Quebec, Canada, we investigated 45 lawns infested with hairy chinch bug, Blissus leucopterus hirtus Montandon, to identify lawn parameters related to its distribution and abundance. Kentucky bluegrass, creeping bentgrass, and perennial ryegrass, respectively, accounted for 55.8, 19.6, and 9.3% of the grass species. Chinch bug population density was associated positively with abundance of perennial ryegrass, whereas it was marginally negatively related with the abundance of creeping bentgrass. An index of the severity of chinch bug infestation was obtained for each lawn by combining estimates of number of infested patches per lawn, average size of the patches, and chinch bug number per patch. The index was associated positively with abundance of Kentucky bluegrass and perennial ryegrass. There was evidence that abundance of creeping bentgrass was associated negatively with the number of infested patches per lawn, area of the patches, and number of chinch bugs within those patches. The number of infested patches increased, whereas patch area and chinch bug number per patch tended to decrease, when broad-leaf weeds were more abundant on a lawn. No significant relationship was found between thatch thickness and patterns of chinch bug abundance and distribution. These results suggest that management of lawns to respectively increase and decrease abundance of creeping bentgrass and perennial ryegrass could facilitate control of hairy chinch bug populations in cool-humid regions.
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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 | 0.000 |
| 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 | 0.000 |
| 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".