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Record W2032493365 · doi:10.1002/qre.1750

One‐sided Control Charts Based on Precedence and Weighted Precedence Statistics

2014· article· en· W2032493365 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 · 2014
Typearticle
Languageen
FieldDecision Sciences
TopicAdvanced Statistical Process Monitoring
Canadian institutionsMcMaster University
Fundersnot available
KeywordsControl chartStatisticsStatisticStatistical process controlChartComputer scienceControl limitsControl (management)Constant false alarm rateProcess (computing)MathematicsAlgorithmArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, we study one‐sided control charts based on precedence statistics. Because we focus on the problem of detecting ‘smaller’ lifetime than expected, we consider only one‐sided control charts that have not received much attention in the literature. Alarm rate and average run length are derived when the process is in control and also when the process is out of control for two Lehmann alternatives. On the basis of the alarm rate and average run length, we propose suitable randomized procedures to determine the best precedence control chart. Control charts based on weighted precedence statistics are then studied. The charts developed here are illustrated with coal mining disasters data. Finally, a comparison of the performance of these two charts is made with that of a Wilcoxon–Mann–Whitney control chart and a cumulative sum chart based on the precedence statistic, and some conclusions are drawn. Copyright © 2014 John Wiley & 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)0.033
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.894
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.033
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 head0.056
GPT teacher head0.379
Teacher spread0.323 · 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