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Record W2972515818 · doi:10.1177/0954405419875355

A study on sustainability assessment of welding processes

2019· article· en· W2972515818 on OpenAlex
Jaber Jamal, Basil M. Darras, Hossam A. Kishawy

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

VenueProceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsWeldingSustainabilityProcess (computing)Gas tungsten arc weldingArc weldingMechanical engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

The concept of “sustainability” has recently risen to take the old concept of going “green” further. This article presents general methodologies for sustainability assessments. These were then adapted to measure and assess the sustainability of welding processes through building a complete framework, to determine the best welding process for a particular application. To apply this methodology, data about the welding processes would be collected and segregated into four categories: environmental impact, economic impact, social impact, and physical performance. The performance of each category would then be aggregated into a single sustainability score. To demonstrate the capability of this methodology, case studies of three different welding processes were performed. Friction stir welding obtained the highest overall sustainability score compared to gas tungsten arc welding and gas metal arc welding.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.679
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.009
GPT teacher head0.228
Teacher spread0.219 · 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