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Record W2315625626 · doi:10.1016/j.neucom.2015.08.111

<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si0002.gif" overflow="scroll"><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mi>p</mml:mi></mml:mrow></mml:msub><mml:mi mathvariant="normal">-norm</mml:mi></mml:math> and sample constraint based feature selection and classification for AD diagnosis

2016· article· lv· W2315625626 on OpenAlexfundno aff
Mingxing Zhang, Yang Yang, Hanwang Zhang, Fumin Shen, Dongxiang Zhang

Bibliographic record

VenueNeurocomputing · 2016
Typearticle
Languagelv
FieldBiochemistry, Genetics and Molecular Biology
TopicMachine Learning in Bioinformatics
Canadian institutionsnot available
FundersNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchNational Institutes of HealthGenentechNational Natural Science Foundation of ChinaNorthern California Institute for Research and EducationAlzheimer's Disease Neuroimaging InitiativeGE HealthcarePfizerBioClinicaBiogenNovartis Pharmaceuticals CorporationU.S. Department of DefenseEli Lilly and CompanyBristol-Myers SquibbF. Hoffmann-La RocheMerckAlzheimer's Drug Discovery FoundationMeso Scale DiagnosticsTakeda Pharmaceutical CompanyAbbVieNational Institute on AgingFujirebio EuropeAlzheimer's Association
KeywordsNorm (philosophy)Feature selectionComputer scienceArtificial intelligenceAdaptabilityFeature (linguistics)AlgorithmPattern recognition (psychology)Flexibility (engineering)Machine learningMathematicsData miningStatistics

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.561
Threshold uncertainty score0.627

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0040.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.5610.487

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 head0.014
GPT teacher head0.236
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations17
Published2016
Admission routes1
Has abstractno

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