Current Status of C&D Waste Management and Green Building Standards in Canada
Bibliographic record
Abstract
The research and literature reviews in the recent years clearly noted the immense economical, social, and environmental impacts created by the construction waste. Many developed countries have developed strategies and mechanisms to reduce the landfill deposits of construction and demolition (C&D) waste. Many European countries and Japan have been practicing C&D waste management for a much longer period and have effectively increased the percentage of recycled and/or re-used wastes in C&D. Landfilling construction waste materials has been recognized as a threat to the environment, especially due to its impact on groundwater quality. In addition, many previous researchers have clearly noted the immense economical, social, and environmental impact created by the construction waste. It was noted that the amount of recycled and/or reused waste use in the Canadian construction industry is much lesser than many other developed nations. Canada recycles and/or reuses only 22% of the solid waste while Denmark recycles and/or reuses 95% of the solid waste. There are many green building rating systems and standards- such as LEED, BREEM, CASBEE, and GreenStar - in the world. The LEED standards are quite popular in Canada. Each rating system uses different methods and weights to analyze the sustainability in construction projects. Although many Canadian construction projects use the LEED `ratings' to assess sustainability, it does not have a broad focus on C&D waste. The need for comprehensive and integrated waste management mechanisms, technologies, rating systems, and policies is widely visible in Canada comparing to some European countries.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.021 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".