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Record W7154317911 · doi:10.14419/ijet.v7i4.20667

Applying Zero Waste Management Concept in a City of Indonesia: A Literature Review

2019· article· en· W7154317911 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Engineering & Technology · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsnot available
Fundersnot available
KeywordsZero wasteLeachateMunicipal solid wasteWaste collectionWaste treatmentCleaner productionInert wasteWaste disposal

Abstract

fetched live from OpenAlex

City waste management in Indonesia still faces loads of challenges, mainly in the case the ultimate disposal (landfill) availability. Only 60-70% of the waste can be transported and disposed of to landfill, while the rest are scattered in various places. Waste dumped in landfill emits leachate contaminating and greenhouse gases. Also, the discarded material is a waste of non-renewable natural resource. Holistic management is necessary, starting upstream to downstream waste management. The concept of Zero Waste offers waste management, initial from the avoiding of trash, recycling, reduction and recovery of second-hand material. Some cities in the world such as Canberra, Adelaide (Australia), Stockholm (Sweden), Nova-Scotia (Canada) and San Francisco (USA) has set a target of Zero Waste. Indonesia still implements management that emphasizes the waste management disposal in a landfill. This literature review examines to find out whether Indonesia can apply the concept of Zero Waste in the upcoming.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score0.410

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 head0.004
GPT teacher head0.225
Teacher spread0.221 · 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