Bibliographic record
Abstract
Twenty percent of all the material delivered to construction and demolition (C&D) disposal sites in the Province of Nova Scotia, Canada, is waste gypsum wallboard (WGW) (Dillon Consulting Ltd., 2006). This is typically in the form of residential or business demolition waste, which includes WGW from new construction activities. This study looked at the use of papered and de-papered waste gypsum wallboard in compost to evaluate its impact on the process, total heavy metal concentration, bioavailable metal concentration, and movement of heavy metals. The study consisted of three components: a short term mechanical in-vessel compost sub-study to assess the impact of composting WGW; a lysimeter cell sub-study to evaluate potential movement of compost constituents from compost to soil and water under a static compost system open to the ambient environmental conditions; and, a final sub-study to determine the performance of waste gypsum wallboard in compost under controlled composting conditions. The study found that the inclusion of up to 34% (by mass) WGW had no negative effects on the degradation of carbon, final pH, and final electrical conductivity in the compost product, however, WGW-containing composts did increase concentrations of total sulphur. There was the potential for elevated levels of total lead and cadmium but the compost produced was within the CCME Class A guidelines for heavy metal concentration. Waste gypsum wallboard containing composts also had increased levels of bioavailable cadmium compared to non-WGW composts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".