Bibliographic record
Abstract
The fifth chapter presents five waste-to-energy (WtE) case studies with data based on reports from technical visits made by the author to these facilities in the Americas. These case studies present technical data and specifications, such as the capacity and type of input waste, combustion chamber conditions, steam parameters, electrical efficiency, flue-gas cleaning systems, bottom and fly ash treatment processes, stack emissions, etc., and, additionally, basic financial data such as investment costs, the gate/tipping fee(s), and feed-in tariffs on electricity. Many photographs, taken by the author, are included. The relevant WtE Plants are: two in the United States, one in Union County, New Jersey, and the newest one, operating since 2016, in West Palm Beach, Florida; and the newest plant in the Americas, the DYEC WtE Plant near Toronto, Canada (including the impressive online stack emissions in the main plant entrance). Innovation opportunities in Latin America and the Caribbean are considered; especially the site visit at the combustion laboratory in Cienfuegos, Cuba, in addition to the potential for WtE plants in Cuba that was addressed through relevant intensive courses and seminars held in 2016 in Havana and Cienfuegos. In the current chapter, technico-economic data for three waste-to-energy (WTE) plants are presented. The data are based in the reports made by the author after his relevant site visits.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".