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Record W4408964877 · doi:10.1016/j.clwas.2025.100273

Green waste, an untapped energy source? Reviewing the prospect of green waste as a biomass energy source

2025· article· en· W4408964877 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

VenueCleaner Waste Systems · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsWaste managementGreen wasteBiomass (ecology)Energy sourceRenewable energyEnvironmental scienceWaste-to-energyMunicipal solid wasteEngineeringFossil fuelEcology

Abstract

fetched live from OpenAlex

Previous research on the assessments of how best to handle the organic fraction of municipal solid waste have concluded that anaerobic digestion is the best waste treatment strategy. This is because the waste steams examined are predominantly made up of food waste. There has been a lack of consideration for green waste streams, which require a different handling approach. This paper offers the first step towards better utilization of green waste by systematically reviewing its potential as a biomass energy source. From a pool of over 770 studies, a collection of 18 studies with test results from 30 green waste samples was compiled. The analysis covers both separated green waste streams and mixed organic streams with high green waste fractions (<50 %), as well as different processing techniques such as screening and composting. The findings confirm that green waste could be a valuable biomass energy source, with separated green waste steams showing the highest potential.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.011
GPT teacher head0.233
Teacher spread0.222 · 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