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Record W1607636450 · doi:10.4271/2000-01-2677

WIzARD: Weld Inspection And Reporting Database Software development, implementation, and application

2000· article· en· W1607636450 on OpenAlexaff
Campbell Steven Piper

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2000
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsChrysler (Canada)
Fundersnot available
KeywordsWizardComputer scienceDatabaseSoftware engineeringSoftwareSoftware developmentEngineering drawingEngineeringProgramming languageWorld Wide Web

Abstract

fetched live from OpenAlex

A relational database created for biw resistance spotweld program. Graphics and data are combined to provide clear and accurate information to cross functional welding teams (inspection, production, maintenance, engineering). System is networked across the biw to enable timely communication of issues. Online input and updating allows tracking of process parameters and assists in continuous improvement. “from-the-floor” documentation creates useful short/long term statistical process control and “hot-spot” identification. Dramatic improvments in team response time, coordination, and lower operating costs through continuous improvement and reduced reprocessing.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.076
Threshold uncertainty score0.253

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0010.000
Scholarly communication0.0040.003
Open science0.0050.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0760.072

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 head0.012
GPT teacher head0.256
Teacher spread0.244 · 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 designNot applicable
Domainnot available
GenreSoftware

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

Citations0
Published2000
Admission routes1
Has abstractyes

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