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Record W4248838280 · doi:10.1002/9781119516651.ch21

Statistical Quality Control—Phase II Control Charts

2020· other· en· W4248838280 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
Typeother
Languageen
FieldDecision Sciences
TopicAdvanced Statistical Process Monitoring
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCUSUMEWMA chartControl chartShewhart individuals control chartStatistical process controlComputer scienceStatisticsSet (abstract data type)Control (management)ChartProcess (computing)MathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

This chapter explains basic concepts of the CUSUM control chart, and discusses two-sided CUSUM control charts using numerical procedures and the fast initial response CUSUM control chart. It also discusses combined Shewhart–CUSUM control charts, CUSUM control chart for controlling process variability, moving average (MA) control charts, and exponentially weighted moving average (EWMA) control charts. The chapter helps the readers to understand the difference between phase I and phase II control charts and to set up one-sided and two-sided CUSUM control charts. It also helps them to set up one-sided and two-sided tabular CUSUM control charts, to understand the advantages of CUSUM control charts over X¯ and R control charts, to set up MA control charts and to set up EWMA control charts.

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)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.621
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0390.003

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 head0.151
GPT teacher head0.504
Teacher spread0.353 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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