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Record W2010277168 · doi:10.1002/qre.1112

Exploring process capability with <i>Mathematica</i>

2010· article· en· W2010277168 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

VenueQuality and Reliability Engineering International · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicAdvanced Statistical Process Monitoring
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsProcess (computing)Computer scienceSoftwareSoftware packageIndustrial engineeringSoftware engineeringEngineering drawingProgramming languageEngineering

Abstract

fetched live from OpenAlex

Abstract Several methods for estimating, assessing and monitoring process capability are illustrated using the software package Mathematica 7 ( Mathematica 7 . Wolfram Media: Champaign, IL, 2009). Graphical techniques that allow assessment of the underlying distributional properties of the process data as well as process capability are presented and illustrated. Several new conventions are proposed that attempt to provide insights into the process with diagnostic tools useful for, but not limited to the manufacturing sector. Several estimation and inferential techniques for the more common indices are presented with tools for determining associated values and the resulting inferences. This notebook is best viewed using Mathematica 7 or Mathematica Player 7. Mathematica Player 7 is a free download available at www.Wolfram.com/products/player/ that allows all features of the notebook to be viewed. Copyright © 2010 John Wiley &amp; Sons, Ltd.

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)0.003
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.016
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 integrity0.0000.001
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 head0.161
GPT teacher head0.413
Teacher spread0.252 · 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