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Record W4283725948 · doi:10.46754/jssm.2022.06.010

QUANTIFICATION ASSESSMENT OF MUNICIPAL SOLID WASTE AS AN EVALUATION APROPOS OF SUSTAINABLE WASTE MANAGEMENT IN KUCHING

2022· article· en· W4283725948 on OpenAlexaff
YU WEE LEE, Soh Fong Lim, TEO PANG CHOW, David Chua

Bibliographic record

VenueJournal of Sustainability Science and Management · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsNuclear Waste Management Organization
FundersUniversiti Malaysia Sarawak
KeywordsMunicipal solid wasteCapital cityWaste managementSolid waste managementWaste collectionWaste streamEnvironmental scienceGeographyEngineering

Abstract

fetched live from OpenAlex

The quantification and characterisation of municipal solid waste are indispensables for waste management forethought. This study quantified the municipal solid wastes from three principal council areas in Kuching, the capital city of Sarawak, Malaysia which are the Kuching South City Council, Kuching North City Hall and Padawan Municipal Council to evaluate and analyse the contemporary waste trend and differentiate between the waste streams. The municipal solid waste samples are amassed directly from the source location and categorised according to the socio-economic level of the sampling location sites. This study discovered that there is no significant difference in the waste composition trend generated by the residents in different residential areas. The composition of the solid wastes was found to vary in different socio-economic categories. Organic waste is found to be the highest waste component in all socio-economic groups. The top three municipal waste compositions from the residential areas are organic wastes (61.58% w/w), plastics (12.06% w/w) and nappies/sanitary napkins (11.67% w/w), which ranged from 44.57% to 72.08%. This study provides a recent waste trend database with a detailed analysis of the differences between the waste streams for sustainable waste management in Kuching.

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)0.020
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.003
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 head0.020
GPT teacher head0.340
Teacher spread0.321 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2022
Admission routes1
Has abstractyes

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