Public preferences for water-conserving groundcovers on verges
Bibliographic record
Abstract
Adapting to changes in water availability is becoming an increasingly important environmental management objective in many regions around the world. One way for cities to conserve water is to enhance drought-resistant vegetation cover. This revegetation practice can take place on many types of land, including road-side verges (also known as nature strips or boulevards), which, in Western Australia, are publicly owned but managed privately by residents of adjoining properties. As preferences for alternative verge groundcovers are not well understood, designing solutions that help achieve environmental goals and satisfy communities is a challenge. We survey community members in Perth, Western Australia, and find that peoples’ preferences for verge landscape design are largely bimodal, and can be classified under two dominant groups of people: those who prefer native vegetation, which requires little irrigation, and those who prefer watered grass. Neighbourhood norms prevail in their rankings, where individuals whose neighbours have planted water-conserving native vegetation gardens on their verges are also more likely to prefer ecological landscape designs. Increasing the extent of gardens that feature water-conserving native plants in high-profile public areas may further increase community acceptance of low water-use groundcovers, and may assist in driving changes in landscape management practices towards having more ecological landscape designs on verges, and beyond.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.005 | 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".