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Record W4249980858 · doi:10.2307/1271083

Dynamic Response of Residuals to External Deviations in a Controlled Production Process

2000· article· en· W4249980858 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueTechnometrics · 2000
Typearticle
Languageen
FieldDecision Sciences
TopicAdvanced Statistical Process Monitoring
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsResidualProcess (computing)Control chartAutocorrelationProcess controlComputer scienceChartProperty (philosophy)Autoregressive integrated moving averageStatistical process controlControl theory (sociology)MathematicsStatisticsControl (management)AlgorithmTime seriesArtificial intelligence

Abstract

fetched live from OpenAlex

We present a general method for studying residual behavior of controlled autocorrelated processes subject to special-cause changes. Understanding residual response to a particular type of process change is important for selecting a proper control chart to monitor the process effectively. The method is introduced by analyzing the residual behavior of a controlled machining process described by a state-space model that has a simple structure and is representative of many real processes. The general method is then developed and applied to an ARIMA(p, l, q) process and to some special processes whose residual behavior has been investigated in the literature. Residual response to a general disturbance sequence is determined, and it is shown that the residual properties are independent of the type of feedback control applied to the process. Implications of this property for process control and monitoring are discussed.

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)0.004
metaresearch head score (Gemma)0.082
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.893
Threshold uncertainty score0.925

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.082
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.014
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.064
GPT teacher head0.442
Teacher spread0.379 · 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