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Record W3033835801 · doi:10.1016/j.neucom.2020.05.078

Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications

2020· article· en· W3033835801 on OpenAlexfundno aff
J. M. Górriz, Javier Ramı́rez, Andrés Ortíz, Francisco J. Martínez-Murcia, F. Segovia, John Suckling, Matthew Leming, Yudong Zhang, José R. Álvarez, Guido Bologna, Paula Bonomini, Fernando E. Casado, David Charte, Francisco Charte, Ricardo Contreras, Alfredo Cuesta‐Infante, Richard J. Duro, Antonio Fernández‐Caballero, Pedro Gómez‐Vilda, Manuel Graña, Francisco Herrera, Roberto Iglesias, Anna Lekova, Ezequiel López‐Rubio, Rafael Martínez‐Tomás, Miguel A. Molina‐Cabello, Antonio S. Montemayor, Paulo Nováis, Daniel Palacios‐Alonso, Juan José Pantrigo, Bryson Payne, Félix de la Paz López, M. Angélica Pinninghoff, M. Rincón, José Sántos, Karl Thurnhofer‐Hemsi, Athanasios Tsanas, Ramiro Varela, José Manuel Ferrández

Bibliographic record

VenueNeurocomputing · 2020
Typearticle
Languageen
FieldComputer Science
TopicAnomaly Detection Techniques and Applications
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchNational Institutes of HealthGenentechIXICOH. Lundbeck A/SServierEisaiNorthern California Institute for Research and EducationUniversity of California, San DiegoUniversidad de GranadaPfizerBiogenBioClinicaF. Hoffmann-La RocheComunidad de MadridUniversity of Southern CaliforniaEli Lilly and CompanyU.S. Department of DefenseMeso Scale DiagnosticsAlzheimer's Disease Neuroimaging InitiativeNovartis Pharmaceuticals CorporationBristol-Myers SquibbAlzheimer's AssociationMichael J. Fox Foundation for Parkinson's Research
KeywordsArtificial intelligenceField (mathematics)Computer scienceRoboticsApplications of artificial intelligenceData scienceArtificial Intelligence SystemRobot

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.008
Science and technology studies0.0010.005
Scholarly communication0.0080.012
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.001

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 head0.047
GPT teacher head0.349
Teacher spread0.301 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations313
Published2020
Admission routes1
Has abstractyes

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