Roadmap to Brownfield Remediation for Urban Agriculture in Kingston, Ontario
Bibliographic record
Abstract
As Canada continues to transition towards deindustrialisation, brownfields have become a problematic by-product that causes environmental and social issues within cities. Brownfields are especially prone to degrade communities in marginalized areas. The goal of this research is to determine the best practices for urban agriculture on brownfield sites in Kingston, Ontario. The City of Kingston currently has a Community Improvement Plan that outlines the policies and procedures in regard to brownfield remediation. Kingston has no urban agriculture on brownfield sites and there are gaps in the policies that guide the city on the most appropriate way to conduct a project like this on contaminated lands. This study was conducted by analyzing Ontario and Kingston government reports to determine the policies that regulate urban agriculture on brownfield sites. Case studies were selected to determine the best practices for urban agriculture in the Canadian context. \nThe findings demonstrate that urban agriculture on brownfields is an integral component to Kingston Ontario sustainable growth. Whether the property is for mixed use such as housing and agriculture or solely agricultural, it promotes civic engagement and connects the community with the land and ecosystem.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.004 |
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".