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ENVIRONMENT-BASED DESIGN (EBD) APPROACH TO IDENTIFY CRITICAL ISSUES IN MANAGING MUNICIPAL SOLID WASTE: NAIROBI, KENYAN CASE STUDY

2021· article· en· W3198560570 on OpenAlexaff
Wenhang Du, Jiami Yang, Hua Ge, Jun‐Juh Yan, Nadia Bhuiyan, Xizhao LIU, Fengjiao Zhou, Yong Zeng

Bibliographic record

Venue˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · 2021
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsConcordia University
Fundersnot available
KeywordsIdentification (biology)KenyaEnvironmental planningBusinessRisk analysis (engineering)EngineeringComputer scienceGeographyPolitical scienceEcology

Abstract

fetched live from OpenAlex

Abstract. As the urban population grows and the global economy develops, municipal solid waste management (MSWM) has become an increasingly prominent global issue. MSWM is particularly difficult in developing countries due to its high cost and time-consuming nature. The Environment-based design (EBD) can potentially contribute to global MSWM by reducing costs and increasing efficiency, especially in helping developing countries identify critical issues in MSWM. This paper uses and demonstrates the effectiveness and efficiency of the EBD method to analyze and identify critical issues in MSWM, using the city of Nairobi as a case study. This paper contains the first two of EBD's three activities: environment analysis and conflict identification, during the design problem period, design knowledge, and design solutions simultaneously and interdependently evolve as a part of the environment. The comparison with the existing literature confirms that the conclusions reached are, to some extent, reliable, time-saving, and less costly, which will offer a possibility to solve the problem of MSWM in impoverished areas.

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)0.002
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.948
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.026
GPT teacher head0.297
Teacher spread0.271 · 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.

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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venue˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesSame topicSustainable Building Design and AssessmentFrench-language works237,207