Assessing Land Use Planning Tools to Mitigate Odour and Lighting Nuisance Related to Cannabis Production
Bibliographic record
Abstract
Cannabis production has expanded significantly across southern Ontario with the legalisation of theindustry. Much of this expansion has occurred within the rural countryside, through the utilisation of existinggreenhouse infrastructure. While the growth of this sector provides economic benefits to rural communities, complaints from adjacent residents related to lighting and odour issues are common and mitigation of such issues is complex. Land use planning policies have been established across southern Ontario to manage the development of cannabis greenhouses; however, policies vary by region and countyand the appropriateness of these policies have not been tested. This study seeks to analyse municipalplanning policies that regulate cannabis production and understand the impacts of these policies on sector,adjacent land owners and rural communities. Planning policies will be analysed at the municipal, regional orcounty level, with the creation of a database to highlight consistency and differences between communities. Case studies will be utilised to gain better insights into the challenges and opportunities related to cannabis production and planning mitigation. This presentation will provide a summary of current research findings, including highlights of a municipal scan of zoning by-law policies and informalinsights into policy appeals in southern Ontario.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".