MétaCan
Menu
Back to cohort
Record W2024442437 · doi:10.1198/tech.2010.09037

Leveraged Gauge R&R Studies

2010· article· en· W2024442437 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

VenueTechnometrics · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Measurement and Uncertainty Evaluation
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRepeatabilityEstimatorOperator (biology)MathematicsStandard deviationStatisticsGauge (firearms)Baseline (sea)Set (abstract data type)Sample size determinationSample (material)MultipleInteger (computer science)Computer scienceArithmeticPhysics

Abstract

fetched live from OpenAlex

To assess measurement system variation, we propose an alternative to the standard gauge reproducibility and repeatability (GR&R) study. The new plan, called a leveraged GR&R Study, is conducted in two stages. In the baseline stage, we select a sample of parts that are measured once only each using a fixed number of operators. Then we deliberately select extreme parts for the second stage where each operator measures each selected part a number of times. We demonstrate the advantages of the leveraged over the standard plan by comparing the standard deviations of the estimators of the parameters of interest. For a fixed number of operators and total number of measurements, we recommend leveraged plans with a baseline size that is roughly half the total number of measurements. We also recommend that the number of parts selected for the second stage be set to an integer multiple of the number of operators and that each of these parts be measured two or three times by each operator. This article has supplementary material online.

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)0.015
metaresearch head score (Gemma)0.070
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.551
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.070
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.010
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.741
GPT teacher head0.555
Teacher spread0.186 · 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