Reducing human health and environmental risks associated with fungicide use on golf courses
Bibliographic record
Abstract
Abstract The widespread use of pesticides on golf courses, essential for maintaining healthy turfgrass, has raised significant environmental and health concerns as perceived by many well‐intentioned members of society. In response to regulatory pressures, this study aimed to evaluate reduced‐risk approaches to managing two prevalent turf diseases, dollar spot ( Clarireedia spp.) and snow molds ( Typhula sp. and Microdochium nivale (Fr.) Samuels & I.C. Hallett), at two experimental sites in Quebec, Canada. A variety of treatments, including synthetic fungicides, reduced‐risk fungicides, biofungicides, and fertilizers, were assessed in 2021 and 2022. For dollar spot, the reduced‐risk fungicide isofetamid achieved similar disease control comparable to the synthetic standard (chlorothalonil/propiconazole); however, biofungicides and fertilizers did not provide significant disease reduction. For snow mold, a reduced‐risk treatment (fluoxastrobin + mefentrifluconazole) was effective but less so than the synthetic standard. Overall, reduced‐risk fungicides demonstrated potential for controlling these diseases while significantly reducing human health and environmental risks, although efficacy varied among those fungicides. This study supports the viability of integrating reduced‐risk or lower‐toxicity pesticides into golf course pest management strategies and programs, contributing to more sustainable turfgrass management practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".