RDF/SRF Evolution in the MSW sector: coexistence of BMT and selective collection
Bibliographic record
Abstract
Mechanical biological treatments (MBTs) are extensively used for managing municipal solid waste (MSW).There are four different methods: fertilizer or compost-like output production, biogas/energy production, wastederived fuel production and disposal in landfills.One issue is the varying characteristics of the waste fed over the lifetime of the plant.This problem is only partially related to the composition dynamics of the generated MSW.Indeed, the main source of input fluctuation of the plant is a result of the implementation of selective collection (SC) strategies, which modify the composition of residual MSW (RMSW).Often the SC strategies are not developed in harmony with the presence or planning of treatment plants, which can consequently suffer from significant variations.A lack of optimization in MSW management strategies and the implementation of new more stringent regulations applied to the final solid products from MBTs could result in a higher tariff for the users.This paper analyses these two problems in terms of two SC scenarios.The consequent effects on the composition of RMSW and on the performance of bio-drying (one of the MBTs options) are discussed.The effect of different SC strategies of MSW is analysed also in terms of RMSW suitability to be converted into refuse derived fuel/solid recovered fuel with simplified treatments.The role of respirometry is also discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".