Best practice diversion and end use options for construction, demolition and renovation clean wood waste
Bibliographic record
Abstract
With a goal of informing the City of Vancouver’s solid waste management strategy, this report explores the best practice options for the separation and processing of clean wood waste from from construction, demolition and renovation activities and the potential end use options for these diverted wood materials. The policy instruments that effectively encourage these actions in jurisdictions similar to Vancouver were also identified. A literature review of academic articles, government documents, and consultant reports informed this research. The recommendations for the City of Vancouver, explored in further detail in the report, are as follows: • Source separation of wood waste is the most effective diversion strategy. By encouraging deconstruction methods over traditional demolition practices, the ease and efficiency of source separation can be improved considerably. • The market for recycled and reused construction and demolition waste products needs to be expanded. Establishing demand for these materials can be accomplished through institutional purchasing and encouraging a diversity of end use applications. • Developing market support infrastructure and expanding the distribution network for reused and recycled building materials is critical for raising their market share. The City’s Deconstruction Hub is a great starting point for expanding the local market capacity for reused and recycled wood building materials.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".