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Record W4296078748 · doi:10.29173/mocs281

Process evaluation of construction methods to quantify safety

2022· article· en· W4296078748 on OpenAlexaffvenue
Nicole Odo, Jeff H. Rankin

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2022
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsProcess (computing)General partnershipProduct (mathematics)Computer scienceVariety (cybernetics)Construction industryConstruction engineeringSystems engineeringEngineeringRisk analysis (engineering)Process managementBusiness

Abstract

fetched live from OpenAlex

The use of off-site construction methods is increasing within the construction industry. While this method has been captured by a variety of terms, they all refer to the process of producing project components in a manufacturing-like facility off-site and transporting completed units to site to be assembled to achieve the desired end-product. To support the quantification of safety performance in off-site construction versus conventional on-site methods, the research has developed a generalized model for capturing and evaluating construction methods. The methodology was developed in partnership with local practitioners to define, assess and compare on-site and off-site construction practices with a safety lens, and the methodology is partially validated in collaboration with various project owners to assess case study projects that employed off-site construction into their processes. The evaluation methodology takes a construction product-focused approach with emphasis on defining a complete material supply chain and capturing the data needed to support quantifiable safety evaluations of the process. As such, the approach takes a unique approach to establish an evaluation methodology for future comparisons.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.458
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.097
GPT teacher head0.492
Teacher spread0.395 · 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 teacher head, 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

Citations1
Published2022
Admission routes2
Has abstractyes

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