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Record W1970222772 · doi:10.1021/ie050790r

Relative Gain Array for Norm-Bounded Uncertain Systems

2006· article· en· W1970222772 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

VenueIndustrial & Engineering Chemistry Research · 2006
Typearticle
Languageen
FieldDecision Sciences
TopicProbabilistic and Robust Engineering Design
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBounded functionNorm (philosophy)MathematicsRepresentation (politics)Control theory (sociology)Set (abstract data type)Computer scienceMathematical optimizationApplied mathematicsMathematical analysisArtificial intelligence

Abstract

fetched live from OpenAlex

This paper considers the extension of relative gain array (RGA) to norm-bounded uncertain systems. We present a method for calculating a tight bound on the worst-case relative gain and derive necessary and sufficient conditions for the sign change of the relative gain over the uncertainty set. The proposed results improve on recently published results [Chen and Seborg, AIChE J. 2002, 48, 302]. More importantly, it is shown that the role of RGA is limited for ascertaining the integrity of uncertain systems. This conclusion is in direct contrast with the corresponding result for adjudging integrity of nominal systems, where the usefulness of RGA is well-known. As an offshoot, we present a signal-based representation of the relative gain for uncertain systems.

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)0.008
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.800
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.327
GPT teacher head0.416
Teacher spread0.089 · 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