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Record W2094806835 · doi:10.1198/tast.2009.08042

Teaching Variation Reduction Using a Virtual Manufacturing Environment

2009· article· en· W2094806835 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

VenueThe American Statistician · 2009
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsLoginProcess (computing)Variation (astronomy)Computer scienceSoftwarePlan (archaeology)Reduction (mathematics)Software engineeringManufacturing engineeringEngineeringOperating systemMathematics

Abstract

fetched live from OpenAlex

This article describes the virtual manufacturing environment Watfactory (freely available at http://services03.student.math.uwaterloo.ca:8080/~stat435/login.htm) and discusses its use in teaching process improvement. Watfactory provides a rich and realistic simulation of a manufacturing process and is accessed through a website requiring no software other than a Web browser. With Watfactory, students select, plan, and analyze the data from a sequence of empirical investigations of many different types, with the ultimate goal of reducing variation in the process output. We have found that using Watfactory addresses many shortcomings in traditional teaching methods for both undergraduate and industrial short courses.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.848
Threshold uncertainty score0.354

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 communication0.0000.000
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 head0.014
GPT teacher head0.265
Teacher spread0.251 · 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