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The architecture of reuse

2019· article· en· W2916903255 on OpenAlexaff
Mark Gorgolewski

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Ecological Systems Analysis
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsReuseFlexibility (engineering)ArchitectureCircular economyProcess (computing)Computer scienceEngineering design processArchitectural engineeringEngineeringProcess managementSystems engineeringEcologyMechanical engineering

Abstract

fetched live from OpenAlex

The starting point for designers in a circular system will often be identifying an inventory of potential second use materials and components. They then develop their design ideas around the tectonic characteristics of the materials. This can be seen as a restriction or a positive inspiration for creating meaningful ecological architecture suitable for the circular economy. Since availability of reclaimed materials and components is currently less predictable, flexibility in design and tolerance to alternatives by the project team and owner are important. The building design community needs to review and adapt conventional practices to increase demand for, and effectively integrate, reclaimed materials and components. This paper considers the architectural process implications and opportunities from reuse. As design teams adopt strategies to increase use of reclaimed materials and components, it is likely that the standard project management stages used by design teams may need to be adapted to facilitate a process better suited to circular strategies. New partnerships need to be formed, and new tools developed. Learning from existing projects a series of strategies are reviewed.

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)0.004
metaresearch head score (Gemma)0.004
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0040.035
Scholarly communication0.0120.015
Open science0.0020.008
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0110.003

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 head0.006
GPT teacher head0.181
Teacher spread0.175 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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