Impact of four turf management regimes on arthropod abundance in lawns
Bibliographic record
Abstract
BACKGROUND: Turfgrass management practices, especially the use of chemical pesticides, may be detrimental to beneficial arthropods such as predators and decomposers. However, little is known about the impact of other practices or pest control products on these beneficials. The impact of four different management regimes, consisting of synthetic pesticide cover sprays or combinations of more targeted applications of natural pesticides, on selected groups of non-targeted arthropods in lawns of different age was studied over 3 years. The short-term effect of diazinon and carbaryl on Carabidae and Collembola was also evaluated. RESULTS: Formicidae and Araneae were the most abundant taxa at both sites, representing 74-80% of total captures. With a few short-term exceptions, no persistent and significant difference between turfgrass management regimes on arthropod abundance was observed over the 3 year study. Diazinon and carbaryl significantly reduced Carabidae abundance, but only one year out of three, while Collembola abundance was only transiently affected by carbaryl application in 2003. CONCLUSION: The study showed that practices and products used in the four management regimes did not disrupt the populations of specific groups of arthropods. These results provide useful information to professionals for the development of ecological turf practices to maintain beneficial arthropod abundance and diversity in urban landscapes.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".