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Record W3210241879 · doi:10.32920/ryerson.14648610.v1

Identifying Barriers to Reducing Ontario’s Construction Waste Through Reclamation, Reuse, and Recycling

2021· preprint· en· W3210241879 on OpenAlexaffabout
Joseph Martin Earle

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDemolitionReuseLand reclamationConstruction wasteDemolition wasteWaste managementIncinerationBusinessEnvironmental planningEngineeringCivil engineeringArchitectural engineeringEnvironmental science

Abstract

fetched live from OpenAlex

Construction, renovation, and demolition waste contributes at least one quarter of all waste that is destined to landfill and incineration in Canada. This research hypothesized that residential renovations could play a significant role in decreasing the amount of waste through reuse of used building materials. It therefore sought to identify barriers to recycling, reclamation, and reuse of building materials in the Ontario construction, renovation, and demolition industry. Through a mixed-method survey of green building professionals five primary barriers were discovered. With greater leadership from green building professionals, materials and markets becoming more consistently available, and more buy-in from residential contractors and homeowners conducting renovations these barriers can be overcome and this type of project can help contribute to reduction of construction, renovation, and demolition waste in the province

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.229

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.261
Teacher spread0.240 · 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 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

Citations0
Published2021
Admission routes2
Has abstractyes

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