FREDERIC-BACK PARK, MONTREAL, CANADA: HOW 40 MILLION TONNES OF SOLID WASTE SUPPORT A PUBLIC PARK
Bibliographic record
Abstract
The City of Montreal, Quebec, Canada, took over the management, in 1988, of a former limestone quarry that was also used as landfill site. The surrounding population of this site was exposed to many nuisances related to the rock extraction and transformation and to the landfilling activities. So, the main goal of the city was to rehabilitate this degraded site, build a public park and give it back to the population. The site’s total area covers 192 ha. From this surface, 72 ha were devoted to the landfill. Over the years, 40 million tons of municipal solid waste have been landfilled. Building a park on such a large site that still produces landfill gas and leachate involves several major challenges. The priority was first to control the landfill gas and the leachate to minimize environmental risks and impacts. In parallel, a process involving design workshops, research, testing, brainstorming and topographical models was launched in order to develop the Master Plan for the park construction. The Master Plan provides the framework for teams working on the project, sets the guidelines for the site’s rehabilitation and phase-by-phase transformation based on the principles of sustainable development. The park construction was initiated in the mid nineties. Nowadays, 48 hectares are already open to the population. The Park will be finalized around 2026 and will then be completely accessible to the public. This is the result of a close collaboration between the Department of Parks and the Department of Environment of the City of Montreal.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".