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Record W4252070243 · doi:10.32920/ryerson.14644293

Evaluating the comparability of environmental product declarations for use as a decision-making tool for building designers

2021· preprint· en· W4252070243 on OpenAlexafffundabout
M.D.C. Gelowitz

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsToronto Metropolitan University
FundersOntario Centres of Excellence
KeywordsComparabilityProduct (mathematics)Inclusion (mineral)Risk analysis (engineering)EngineeringBusinessPsychologyMathematicsSocial psychology

Abstract

fetched live from OpenAlex

With its inclusion to LEED®, use of EPDs in the construction industry will accelerate over time. This research examines current practices surrounding the use of EPDs in construction and addresses a key need arising from a case study completed on the first Canadian project to use EPDs. Findings suggest that lack of comparability between claims hinders the ability for them to be used as true, decisive comparative tools on projects. This informed the development of a semi-automated comparison tool for EPDs and PCRs. Three separate construction product categories were chosen for comparison to develop this tool: insulation, flooring, and cladding systems. Comparability was more evident in categories that have had early involvement in publishing environmental claims (such as flooring, because of its human health implications). However, there was concerning evidence regarding the comparability of EPDs that were comparable according to international standards were incomparable according to the comparison framework developed.

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)0.248
metaresearch head score (Gemma)0.549
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.928

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2480.549
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0110.009
Science and technology studies0.0020.004
Scholarly communication0.0080.008
Open science0.0020.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.077
GPT teacher head0.374
Teacher spread0.296 · 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 designObservational
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

Citations3
Published2021
Admission routes3
Has abstractyes

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