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Record W1763578729 · doi:10.5539/jsd.v8n9p14

Residential Construction Sustainability in the UK and Prospects of Knowledge Transfer to Kazakhstan

2015· article· en· W1763578729 on OpenAlexvenueno aff
Serik Tokbolat, Rajnish Kaur Calay

Bibliographic record

VenueJournal of Sustainable Development · 2015
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityBusinessSustainability reportingEnvironmental economicsQuestionnaireEnvironmental planningEconomicsGeographySociology

Abstract

fetched live from OpenAlex

<p>This paper aimed to investigate the up-to-date levels of sustainability in the UK construction with special interest to sustainable housing. It also aims to examine the justification behind construction and housing sustainability, and to look at the practicality of transferring current expertise within the UK as well as to an emerging Central Asian country such as Kazakhstan. A synergy of case studies, survey and numerical simulation research methodologies were applied to undertake a wide-spectrum analysis of the topic. Regardless of difficulties related to applying sustainable practices the considered housing developments are found to be satisfactory in terms of environmental and socio-economic effects. Technical evaluation of the case studies compared to standard housing parameters has shown encouraging outcomes and confirmed the claimed energy and water efficiency. Findings of the survey indicated that construction companies of the UK and Kazakhstan are at different stages of application of sustainability measures. It was also established that companies and public are mainly optimistic about sustainability if suitable economical and legal conditions are ensured. Finally, numerical simulations have shown that selected sustainability measures made the studied housing projects competitive on the sustainability market. </p>

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.011
GPT teacher head0.246
Teacher spread0.234 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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