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Record W4247772332 · doi:10.32920/ryerson.14664510

Comparing Three Building Life Cycle Assessment Tools for the Canadian Construction Industry

2021· preprint· en· W4247772332 on OpenAlexaboutno aff
Hayley Cormick

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsScope (computer science)Life-cycle assessmentComputer scienceMeaning (existential)EstimatorArchitectural engineeringEngineeringStatisticsMathematicsProduction (economics)EconomicsPsychology

Abstract

fetched live from OpenAlex

This research aims to contribute to quantifying whole building life cycle assessment using various software tools to determine how they can aid the construction industry in reducing carbon emissions, and in particular embodied emissions, through analysis and reporting. The conducted research seeks to examine and compare three whole building life cycle assessment tools; Athena Impact Estimator, Tally and One-Click LCA to relate the input variability to the outputs of the three programs. The three whole building life-cycle assessments were conducted using a case study building with an identical bill of materials and compared to determine the applicability and strengths of one program over another. The research confirmed that the three programs output significantly different results given the variability in scope, allowable program inputs and generated “black-box” back-end calculations, where the outputted whole building life cycle carbon equivalents of One-Click LCA is less than half than of Tally meaning the programs outputs cannot be simply compared side-by-side.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score0.710

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0060.005
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.048
GPT teacher head0.289
Teacher spread0.241 · 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 designSimulation or modeling
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 routes1
Has abstractyes

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