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Record W4205806766 · doi:10.32920/ryerson.14652540.v1

Composting as wise waste diversion technique

2021· preprint· en· W4205806766 on OpenAlexaff
Naeem A. Memon

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsWaste managementReuseIncinerationCompostBiodegradable wasteUSableEnvironmental scienceMunicipal solid wasteCleaner productionWaste treatmentGreen wasteMobile incineratorEnvironmentally friendlyWaste streamWaste collectionEngineeringComputer science

Abstract

fetched live from OpenAlex

There are several MSW management approaches, but the most effective are source reduction, recycling, and reuse called (3R), which can prevent or divert materials from the waste stream. Source reduction involves altering the design, manufacture, or use of products and materials to reduce the amount and toxicity of what gets thrown away. The other approaches are recycling and reuse processes, in which inorganic part can be separated to achieve recycled products while, organic waste or green waste can be decomposed to produce usable substance called compost. This alternative approach for handling organic waste turned as wise waste alternative for achieving environmental friendly end product which would reduce waste burden from landfills and creates sustainable environment. Generally, the paper discusses process and importance of organic composting as an alternative approach in reducing and diverting the organic waste burden from the traditional waste disposal methods like, landfill or incineration and analyses its advantages towards the municipalities and local communities in adopting organic waste diversion approach to achieve natural soil conditioner called Compost.

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 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 categoriesMeta-epidemiology (narrow), Open science, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.619
Threshold uncertainty score1.000

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 science0.0010.017
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.001

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.

Opus teacher head0.017
GPT teacher head0.247
Teacher spread0.230 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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