Performance of locally available bulking agents in Newfoundland and Labrador during bench-scale municipal solid waste composting
Bibliographic record
Abstract
Abstract Background Newfoundland and Labrador (NL) has one of the highest waste disposal rates in Canada and it has 200 small communities without access to central composting facilities. During Municipal solid waste (MSW) composting, the selection of bulking agents is critical. Bench-scale composting systems plus locally available bulking agents are thus desired for economic and effective MSW management in NL communities. This study evaluated the performance of locally available bulking agents (i.e., NL sawdust and peat) during MSW composting in a bench-scale system. Physiochemical (temperature, oxygen uptake rate, pH, electrical conductivity, moisture and ash content, and C/N ratio) and biological (enzyme activities and germination index) parameters were monitored to evaluate compost maturity and stability. Results In peat composting, higher temperature for a longer duration was observed, indicating more effective pathogen removal and sterilization. High enzyme activities of dehydrogenase, β-glucosidase, and phosphodiesterase in the third week of composting imply high microbial activity and high decomposition rate. The low C/N ratio for compost product implies acceptable stability states. In sawdust composting, higher temperature and oxygen uptake rate (OUR) were observed in the third week of composting, and higher enzyme activities in the second week. Sawdust composting generated a higher germination index, indicating higher maturity. Conclusions Both sawdust and peat are effective bulking agents for the bench-scale composting. The choice of a bulking agent for a particular community depends on the availability of the agent and land in the region, convenience of transportation, price, and the expected quality of the compost product.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".