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Record W1974281211 · doi:10.2495/sdp-v8-n4-464-484

Significance of sub-criteria in measuring sustainable performance of building envelope development

2013· article· en· W1974281211 on OpenAlexvenueno aff
Abraham Mwasha, Rupert G. Williams, Joseph Iwaro

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2013
Typearticle
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsBuilding envelopeSustainable developmentEnvelope (radar)Environmental scienceArchitectural engineeringEnvironmental planningEnvironmental resource managementBusinessEngineeringGeographyPolitical scienceAerospace engineeringMeteorology

Abstract

fetched live from OpenAlex

Currently, several building performance assessment methods are in use around the world but these methods fail to incorporate the needed sustainable energy performance indicators and sub-criteria, thus the necessity of current lack of capability to determine the actual sustainable performance of building envelope.Besides, aggregate criteria are extremely complex to create and are often criticized for simplifying the complex issues of sustainability into one performance issue.The aim of this paper is to create more effective sub-criteria that can be assessed under these sustainable energy performance indicators and infl uence the capability of building performance assessment methods.To create these sub-criteria, a comprehensive survey of the construction industry professional was conducted using a questionnaire technique while the data was analyzed using correlation and regression analysis techniques.Suggestions were made on those sub-criteria that should be assessed under the sustainable performance indicator to be incorporated into a sustainable performance model for the buildings' envelope development.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.044
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.238
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2013
Admission routes1
Has abstractyes

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