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Record W2996708337 · doi:10.1520/jte20190327

Variable Sampling Interval Control Charts for Monitoring the Ratio of Two Normal Variables

2019· article· en· W2996708337 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

VenueJournal of Testing and Evaluation · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicAdvanced Statistical Process Monitoring
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsControl chartX-bar chartStatisticsChartMathematicsShewhart individuals control chartInterval (graph theory)Control limits\bar x and R chartComputer scienceEWMA chartProcess (computing)

Abstract

fetched live from OpenAlex

Abstract It is well documented that the distribution of the ratio between two random normal variables is asymmetric. As a consequence, the two-sided Shewhart control chart monitoring this ratio (denoted as Shewhart-RZ chart) has an average run length–biased property. In order to overcome this drawback, we propose in this article a design for two separated one-sided control charts. Furthermore, we improve the efficiency of the Shewhart-RZ chart by combining the Shewhart chart with a variable sampling interval strategy. The chart performance is then evaluated by using an average time to signal. The numerical results show that our proposed charts outperform the two-sided Shewhart-RZ control chart in detecting process shifts.

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)0.011
metaresearch head score (Gemma)0.034
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.469
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.034
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 integrity0.0000.000
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 head0.250
GPT teacher head0.470
Teacher spread0.220 · 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