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Record W2486388585 · doi:10.5539/mas.v10n8p98

Evaluation of Compaction Index to Achieve Sustainable Urban Development Using AHP: Two Case Studies

2016· article· en· W2486388585 on OpenAlexvenueno aff
Sona Bikdeli

Bibliographic record

VenueModern Applied Science · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsCompact cityAnalytic hierarchy processSustainable developmentCompactionDiversity (politics)SustainabilityIndex (typography)Urban planningIdeal (ethics)Environmental planningSustainable cityEnvironmental economicsBusinessComputer scienceCivil engineeringGeographyPolitical scienceEconomicsMathematicsOperations researchEngineeringLaw

Abstract

fetched live from OpenAlex

The search for an ideal city, which can express both technological advantages and healthy spirit of rural life based on enlightening ideas of social justice, has long been the major concern of most philosophers, social reformers, writers, architects and urban planners. Urban form is known as a source of environmental problems. The emergence of "sustainable development" as a common term has raised many discussions on urban forms. Different types of urban forms (corridor, compact, marginal and edge) have been evaluated for sustainable urban development. It is revealed that compact city is more sustainable than other forms. There is disagreement on potential effects of compaction. Using archival studies, surveys and questionnaires, the author evaluates the environmental sustainability of Yousefabad as a dense neighborhood, compared to Garnet Hill, by AHP to prove that compaction alone cannot bring the expected advantages. To achieve advantages of compaction in urban design, the author emphasizes that four basic criteria of compact city, density, sustainable transportation, mixed land-use and diversity, should be interrelated.

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)0.006
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.155
GPT teacher head0.407
Teacher spread0.252 · 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 designQualitative
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
Published2016
Admission routes1
Has abstractyes

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