CARCINOGENIC AND GENOTOXIC POTENTIAL OF TURF PESTICIDES COMMONLY USED ON GOLF COURSES
Bibliographic record
Abstract
As a result of the controversy surrounding pesticide use and animal and human health concerns, many municipalities in Canada have restricted, or are in the midst of restricting, the use of pesticides for cosmetic purposes. In some cases, pesticide use on golf courses is also being phased out at the municipal level. One of the dominant health effects of concern in relation to pesticide exposure is the occurrence of cancer. With over 1600 golf courses in Canada and between 400 and 600 new courses created each year in Canada and the United States, there appears to be increasing potential for unintentional human and animal exposure to turf pesticides. In light of the debate around pesticide exposure and the onset of cancer that has lead to controversial Canadian municipal bylaws regulating pesticide use, and due to recent results of a biomonitoring study that has shown genotoxicity in a rodent species living in golf-courses, it seems timely to review the carcinogenic and genotoxic potential of commonly used golf-course pesticides. The purpose of this review is to present some debated epidemiological research that deals with the relationship between pesticide exposure and cancer, and to review and update the literature on the in vivo and in vitro mammalian carcinogenic and genotoxic potential of these pesticides. It is our intention to unite information from various sources so those interested specifically in the carcinogenicity and genotoxicity of pesticides commonly used on golf courses can refer to one comprehensive and updated resource.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".