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Record W2129082993 · doi:10.1109/isie.2011.5984234

Mixed Virtual Reference Feedback Tuning - Iterative Feedback Tuning: Method and laboratory assessment

2011· article· en· W2129082993 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicControl Systems and Identification
Canadian institutionsUniversity of Ottawa
FundersUnitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii
KeywordsHessian matrixComputer scienceControl theory (sociology)Position (finance)Fine-tuningFeedback controlIterative methodControl engineeringControl (management)AlgorithmEngineeringArtificial intelligenceMathematicsPhysics

Abstract

fetched live from OpenAlex

This paper gives a mixed Virtual Reference Feedback Tuning (VRFT) - Iterative Feedback Tuning (IFT) design and tuning method. The performance objectives of VRFT and IFT are discussed comparatively. The parameterization of IFT is employed in the calculation of a convenient unbiased estimate of the Hessian specific to IFT. The VRFT-IFT design and tuning method is applied and assessed in the speed and position control of a laboratory equipment.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.738
Threshold uncertainty score0.671

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.041
GPT teacher head0.261
Teacher spread0.220 · 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

Quick stats

Citations2
Published2011
Admission routes1
Has abstractyes

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