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Record W1995381013 · doi:10.1002/cjs.11203

Selection of partial replication on two‐level orthogonal arrays

2014· article· en· W1995381013 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.

venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCanadian Journal of Statistics · 2014
Typearticle
Languageen
FieldDecision Sciences
TopicOptimal Experimental Design Methods
Canadian institutionsnot available
FundersNational Science Council
KeywordsReplication (statistics)Selection (genetic algorithm)Variance (accounting)Orthogonal arrayVariance componentsComputer scienceStatisticsMathematicsAlgorithmArtificial intelligenceAccounting

Abstract

fetched live from OpenAlex

Abstract Due to the lack of a replication‐based estimate for the experimental error variance, unreplicated orthogonal main‐effect plans (OMEPs) usually appear to be unsatisfactory in screening active factors, when there are certain interactions likely to be non‐negligible. In this study, partially replicated designs constructed through two‐level orthogonal arrays are recommended to compensate for this deficiency. A compound criterion called the extended minimum aberration (EMA) is proposed for choosing the twice‐replicated fractions. According to the EMA criterion, sufficient conditions for constructing the minimal two‐level OMEPs with partial replication are proposed, and a series of EMA designs with practical run‐sizes is provided for practical applications. The Canadian Journal of Statistics 42: 168–183; 2014 © 2014 Statistical Society of Canada

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)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.643
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.008
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)0.0010.000

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 head0.190
GPT teacher head0.420
Teacher spread0.230 · 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