Bibliographic record
Abstract
Abstract Public private partnership has played a mayor role in development and successful operation of the current KMS Peel Waste-to-Energy Plant located in Peel Region, Ontario. On December 10, 1998 KMS Peel Inc. and the Region of Peel entered into an agreement to expand the waste-to-energy facility by 36,000 tonnes (one additional incineration unit). Due to expansion, new, more stringent emission limits were imposed by the latest Ontario Ministry of Environment A-7 Guideline and the Canada-Wide Standards developed by Canadian Council of Ministers of Environment. A Selective Catalytic Reduction (SCR) system with a sodium tetrasulphide injection was selected to supplement the existing dry scrubber/fabric filter air pollution control system for additional reduction in mercury, nitrogen oxides and dioxins/furans emissions. With the upgraded air pollution control technology, the facility will be able to meet the latest emission standards and, to a certain degree, any new standards that may be enforced in future years. This paper outlines a partnership model that has been successfully implemented in Ontario and has contributed to the public accepting waste-to-energy as integral part of the waste management system, ultimately resulting in facility expansion. It also describes the current facility and upgrade to the existing air pollution control system.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 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".