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Record W2904998558 · doi:10.1680/jenes.18.00028

Recovery of recyclables of municipal solid waste: the case of Jordan

2018· article· en· W2904998558 on OpenAlexvenueno aff
Fawzi Suleiman Gharagheer, Moshrik R. Hamdi, Subhi M. Bazlamit

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsnot available
FundersMinistry of Minority Affairs
KeywordsMunicipal solid wasteChristian ministryGeneral partnershipBusinessOperations managementWaste managementFinanceEngineeringPolitical science

Abstract

fetched live from OpenAlex

The low recyclable recovery (RR) rate of municipal solid waste (MSW) (7·0%) in Jordan compared to the average recovery rate of the members of the EU (42%) indicates the necessity of changing the current practices of RR being implemented in Jordan where the latest national strategy of MSW management, launched by the Ministry of Municipal Affairs in May 2015, aims to achieve an RR rate of 50% by the year 2034. This research aims to identify the attributes of an efficient and successful RR programme and recommend an implementable plan for RR that can achieve the target of 50% earlier than the year 2034. Practices of RR currently being implemented in Jordan are analysed and compared with successful practices implemented in developed countries. A public–private partnership is suggested to establish and operate semi-mechanical RR facilities that use the resources currently owned and operated by Joint Service Councils and local municipalities. Utilising the current resources enables the country to achieve the 50% RR rate goal. The rough financial analysis conducted clearly indicates that the 20% RR rate of plastic alone can offset the deficit in the municipality budget resulting from MSW management in Jordan.

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)0.001
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 score0.040
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.008
GPT teacher head0.215
Teacher spread0.207 · 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 designBench or experimental
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

Citations2
Published2018
Admission routes1
Has abstractyes

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