FROM YOUR CAR TO YOUR PATIO: USING RECYCLED TIRE PRODUCTS IN BUILDING PROJECTS
Bibliographic record
Abstract
INTRODUCTION. Canada has dealt with many of the same challenges managing scrap tires as found in other countries; however, with the world's second largest land mass and one of the lowest population densities, the challenges of recycling tires in Canada can be even more daunting.Over 350,000 tons of tires are discarded each year in Canada. That is the equivalent of 35 million passenger car tires—or one tire per Canadian—generated as waste annually. Of this total, the province of Manitoba is responsible for generating 1.3 million scrap tires annually, requiring an in-province environmental management solution. Tire Stewardship Manitoba's role is to ensure that every tire from every corner of the province is collected and processed, as well as finding markets for its recycled tire rubber products.The purpose of this article is to provide the practicing professional some insight into tire recycling in Canada and Tire Stewardship Manitoba's role. It also serves as an introduction to creating more sustainable buildings and landscapes using recycled tire materials and products in new building, road, or landscape projects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".