Drywall (Gyproc Plasterboard) Recycling and Reuse as a Compost-Bulking Agent in Canada and North America: A Review
Bibliographic record
Abstract
The incessant disposal of drywall waste, generated predominantly from construction and demolition sites, has been associated with many environmental problems. In landfill sites, it has long been linked with the generation of hydrogen sulphide, a toxic and foul-smelling gas, while the incineration of this waste results in the potential release of sulphur dioxide gas, a contributor to acid rain formation. The traditional disposal methods also result in the loss of a valuable resource. Therefore, proper management of this waste through recycling programs and subsequent returns to the end market will ensure that a valuable resource is not lost and that environmental impacts are mitigated. Many potential end markets have been identified for recycled drywall. The application as a bulking agent for composting is one of these markets, which could also provide additional calcium and sulphur nutrients to the soil. Despite the benefits of drywall waste recycling, certain challenges have crippled its recycling rate in North America. This review summarises the current situations with drywall recycling and disposal, existing markets, and the availability of competing markets. Furthermore, the potential use of drywall as a compost-bulking agent is discussed. Finally, a possible solution to improving the recycling rate and market demands for drywall is presented.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".