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Record W2626151977 · doi:10.1299/jsmeenv.2016.26.203

MBT (Mechanical Biological Treatment) can be used to help promote heat recovery from RDF.

2016· article· en· W2626151977 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Proceedings of the Symposium on Environmental Engineering · 2016
Typearticle
Languageen
FieldMaterials Science
TopicMetallurgy and Material Science
Canadian institutionsHealth Research Foundation
Fundersnot available
KeywordsRDFRefuse-derived fuelWaste managementGreenhouse gasProcess (computing)Mechanical biological treatmentEnvironmental scienceRaw materialMunicipal solid wasteProcess engineeringComputer scienceEngineeringChemistryWaste collection

Abstract

fetched live from OpenAlex

The solid fuel know as RDF (Refuse Derived Fuel) can be used to recover energy from waste while also helping to reduce greenhouse gas emissions. Although there is an increasing interest in RDF, several issues hinder greater uptake of this technology. We investigated strategies to address these issues, based on surveys and examples of best practice. We found that the process of separating raw kitchen waste from burnable waste addresses many of the issues with RDF. In particular, we believe that MBT (Mechanical Biological Treatment), which combines methane fermentation with RDF production, can be used to help promote heat recovery from RDF. Further investigation is required into MBT technology, including ① testing of waste separation processes and ② trials of energy generated from separated waste to validate case studies.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.193
Teacher spread0.178 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it