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Record W4231805243 · doi:10.1002/9781118928806.ch3

Case Studies

2017· other· en· W4231805243 on OpenAlexaff
Mark Gorgolewski

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicInnovations in Concrete and Construction Materials
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsReuseAdaptive reuseArchitectural engineeringEngineeringCivil engineeringComponent (thermodynamics)ResidenceConstruction engineeringWaste managementSociology

Abstract

fetched live from OpenAlex

This chapter describes a series of projects that all use previously used materials in some way and provides useful lessons for how the industry needs to respond in addressing circular issues. The projects are divided into four categories: reuse what is available at the site, reuse construction materials from elsewhere, find secondary uses for non-construction materials and include adaptive reuse of whole buildings along with component reuse. Hill End Eco-house is a high-end private residence on a river front site that is constructed using many salvaged materials and components, including much of the house it replaced. The Pocono Environmental Education Center designed by Bohlin Cywinski Jackson explores the architectural potential of using shingles fabricated from waste car tyres. The Brighton Waste House is a project initiated by BBM Architects in association with the University of Brighton to demonstrate the potential for using rubbish as a construction material and to establish appropriate applications for unheralded materials.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.971
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.005
Science and technology studies0.0080.002
Scholarly communication0.0030.003
Open science0.0020.004
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0290.005

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 head0.038
GPT teacher head0.294
Teacher spread0.256 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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