Bibliographic record
Abstract
An effective strategy adopted in recent years for municipal solid waste management is the source-separation of solid waste, most commonly into organics, recyclables such as glass, plastics and papers, and refuse.It has been proposed that source-separated organic waste (SSO) is an excellent lignocellulosic biomass of fermentable carbohydrates, and has the potential to serve as a low-cost feedstock for bioconversion into energy products such as ethanol and hydrogen.To evaluate the feasibility of converting SSO to energy products, a better understanding on the energy contents and highly-variable composition of SSO is needed.This paper is based on the results obtained from a ten-month analysis on the SSO collected from the City of Toronto, Ontario, Canada.Detailed analyses on the composition in terms of the VOC, cellulose, hemicellulose, and lignin contents, as well the amounts of carbohydrates, glucose, xylose and other fermentable sugars are carried out.The experimental results show that the average values of moisture content, at 55%, and VOC, from 65% to 96% per dry mass, was sufficiently high to support microbial growth, making it an acceptable feedstock for anaerobic digestion.The results of SSO are compared to other traditional cellulosic feedstock, such as hardwood, agricultural products, food and herbaceous crops, and it was demonstrated that comparable amount of fermentable sugars are contained in SSO.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".