Understanding Distribution Patterns of Lawn Alternatives in Kingston, Ontario
Bibliographic record
Abstract
Residential turfgrass lawns have been associated with wasted water, chemical runoff, increased emissions, and decreased biodiversity. Traditional turfgrass lawns are deeply entrenched in western society’s status quo, and has proven difficult to normalize more ecologically sustainable solutions. The research goal of this paper is to understand and interpret distribution patterns of lawn alternatives in Kingston, Ontario. Lawn alternatives were mapped in 10 neighbourhoods. Neighbourhoods were characterised by their distinct spatial types, developmental context, and selected demographics data. Criteria for defining a lawn alternative was synthesized from previous studies. A classification scheme describing the character of lawn alternatives was developed. The inventory maps provide a previously unavailable snapshot of the types and distribution of lawn alternatives in Kingston, Ontario and are intended to assist the development of enhanced policy. This study found correlations between spatial type, developmental context, income, and lawn alternative coverage and character.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Mapping the distribution of lawn alternatives in a Canadian city; urban ecology and planning.
The dissertation studies lawn alternatives and their distribution in Kingston.
Spatial analysis of residential lawn alternatives; environmental geography, not research practice.
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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".