No Mow May and Leave The Leaves: The impact of social campaigns on turf quality
Bibliographic record
Abstract
Abstract The No Mow May and Leave The Leaves social media campaigns, both of which advocate for a minimalist approach to turf management in various settings including home lawns, parks, and urban greenspaces, have gained momentum in the public consciousness. No Mow May encourages homeowners and municipalities to refrain from mowing the grass in May, and Leave The Leaves recommends refraining from raking and removing fallen tree leaves in the autumn. The underlying goal is to support biodiversity, particularly pollinators, by either allowing wildflowers to flourish or providing an overwintering habitat for insects and other small creatures. This study examined each of these campaigns and the combination of both on turf quality and weed establishment. By avoiding lawn mowing until June, the unintended consequences were increased weed invasion and decreased turf quality. Regardless of Leaf treatment, flower number did not differ between mowed and unmowed treatments except when counting immediately followed mowing. Thick leaf litter over turf in the winter led to turf death followed by weed invasion. The challenges we observed associated with No Mow May and Leave The Leaves initiatives could be addressed by integrating science‐backed practices and considering regional variations in climate and grass species. This would ensure that both environmental conservation and turf health are considered.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".