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Record W2953917655 · doi:10.29173/mocs81

Accessibility-Based Location Selection for Building Panelized Housing for Seniors

2019· article· en· W2953917655 on OpenAlexafffundvenueabout
Yuan Chen, Xianfei Yin, Ahmed Bouferguène, Yuxuan Zhang, Mohamed Al‐Hussein, Bingsheng Liu

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of Alberta
FundersTianjin Science and Technology CommitteeAlberta Innovates
KeywordsUnit (ring theory)Quality (philosophy)Transport engineeringSelection (genetic algorithm)BusinessSubdivisionSet (abstract data type)Environmental economicsArchitectural engineeringComputer scienceEngineeringCivil engineeringEconomics

Abstract

fetched live from OpenAlex

Population ageing is stimulating an increase in the demand for housing suitable for seniors. To meet the demand, the market share of senior housing needs to increase substantially in a relatively short period of time; therefore, panelized construction, as an efficient, economical, and environmentally-friendly construction method, can be regarded as a promising building approach to meet urgent demand for multi-unit housing. However, prior to construction, decisions regarding the location selection for building panelized housing can have a great influence on the level of accessibility that seniors have to neighbouring facilities and services, further affecting their health and quality of life. Based on this, the research presented in this paper aims to search potential land areas for panelized housing developments for seniors from the perspective of accessibility. A set of methods is proposed to define the opportunities and constraints for potential land, measure the accessibility, and select the most suitable location for senior housing by means of suitability analysis. A case study of Edmonton is then analyzed to illustrate the application of these methods.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score0.883

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.016
GPT teacher head0.284
Teacher spread0.268 · 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 designObservational
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

Citations0
Published2019
Admission routes4
Has abstractyes

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