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Record W2022397328 · doi:10.1061/41109(373)30

Exploring the Effects of Context Level Factors on the Structural Steel Fabrication Shop Operation

2010· article· en· W2022397328 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicProduct Development and Customization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsContext (archaeology)FabricationComputer scienceQuality (philosophy)Variety (cybernetics)Risk analysis (engineering)BusinessArtificial intelligence

Abstract

fetched live from OpenAlex

Structural steel fabrication is influenced by both operation and context level effective factors. Usually the mechanisms through which operation level effective factors (e.g. capacity of equipment and operation sequences) affect the fabrication shop are properly documented within a variety of methods such as equipment manuals, job instructions, standards, quality control charts and ISO documents. However, while context level effective factors (e.g. operators' fatigue level and organizational policies) originate from human behavior, usually their enforcing paths are not well understood like operation based effective factors. This issue usually limits decision makers at steel fabrication shops to accept more inaccuracy by ignoring the effects of context level factors during their analyses. In an effort to address this issue, this research attempts to explore the effects of fatigue (as a context level effective factor) and its affecting dynamics within a steel fabrication shop. A hybrid model of system Dynamics and Discrete Event Simulation modeling approaches has been employed as the main tool for the research.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.810
Threshold uncertainty score0.210

Codex and Gemma teacher scores by category

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

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 head0.059
GPT teacher head0.221
Teacher spread0.162 · 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

Quick stats

Citations1
Published2010
Admission routes1
Has abstractyes

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