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Record W2045527432 · doi:10.1155/2008/142013

Advanced Signal Processing and Pattern Recognition Methods for Biometrics

2008· article· en· W2045527432 on OpenAlexaffabout
Nikolaos V. Boulgouris, Juwei Lu, Konstantinos N. Plataniotis, Arun Ross

Bibliographic record

VenueEURASIP Journal on Advances in Signal Processing · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicScientific Research and Discoveries
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWest virginiaBiometricsLibrary scienceDivision (mathematics)EngineeringTelecommunicationsComputer scienceHistoryArchaeologyArtificial intelligence

Abstract

fetched live from OpenAlex

1Department of Electronic Engineering, Division of Engineering, King’s College London, London WC2R 2LS, UK 2Vidient Systems, Inc. 4000 Burton Drive, Santa Clara, CA 95054, USA 3The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada M5S 3G4 4Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA

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.

How this classification was reachedexpand

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
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 head0.061
GPT teacher head0.399
Teacher spread0.338 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2008
Admission routes2
Has abstractyes

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