Golf course pesticide mitigation through design
Bibliographic record
Abstract
Water quality, wildlife habitat, and landscape restoration are becoming major factors in golf course projects. However, golf architects often do not recognize pesticide mitigation as a factor in designing golf courses. The objective of this research was to explore the possibility of incorporating environmental design strategies, which can reduce the effects of pesticides, into golf courses. The strategies were phytoremediation, constructed wetlands, underground collection, and vegetation buffers, which were then judged according to their possible effects on playability, maintenance, and aesthetics. Before these design strategies can be incorporated into the design process, however, it is important to understand their practical applications. Therefore, information was collected by interviewing golf course superintendents to include their professional opinions regarding the four design strategies. Superintendents were sampled from Tucson, Arizona and Vancouver, British Columbia, to explore the potential differences in the applicability of the design strategies. Grounded theory and concept mapping were used to develop patterns in the interview transcripts. This resulted in responses being categorized as either descriptive, design dependent, or design independent. The overall results illustrated the importance of how the design strategies are designed and located within the regional and site context of the golf course. The outcome of this research was a set of practical design dependent guidelines that can be used by golf course architects to incorporate these design strategies into golf courses for pesticide mitigation.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".