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Record W4254222948 · doi:10.1109/tcyb.2019.2918606

IEEE Transactions on Cybernetics

2019· article· en· W4254222948 on OpenAlexaff
Sr Past, Mengchu Zhou Vp-Cybernetics, Sam Kwong Vp-Finance, Ferat Sahin, Andreas Nuernberger, Adrian Stoica Vp, Vladimı́r Mařı́k, Владик Крейнович, Rodney G. Roberts, Robert Woon, Maria Pia Fanti, Keith W. Hipel, Okyay Kaynak, Róbert Kozma, Hideyuki Takagi, György Eigner, Enrique Herrera‐Viedma, Karen Panetta, Ching‐Chih Tsai, Fei‐Yue Wang, Giancarlo Fortino, David Mendonça, Tadahiko Murata, Png Shi, Thomas Strasser, Jun Wang, É Jos, Kathleen Kramer, Joseph Lillie, James Jefferies, Witold Kinsner, Francis Grosz, Geographic Activities, Robert Fish, Association Liu, Thomas Coughlin, Stephen Welby, Thomas Siegert, Business Administration, Julie Cozin, Corporate Governance, Donna Hourican, Jamie Moesch, Sophia Muirhead, Cherif Amirat, Karen Hawkins, Cecelia Jankowski, Michael Forster

Bibliographic record

VenueIEEE Transactions on Cybernetics · 2019
Typearticle
Languageen
FieldComputer Science
TopicInternet of Things and AI
Canadian institutionsCanadian Standards Association
Fundersnot available
KeywordsCyberneticsComputer scienceCognitive scienceArtificial intelligencePsychology

Abstract

fetched live from OpenAlex

closely interrelated fields of man-machine systems, systems science, systems engineering, and cybernetics.All members of the IEEE are eligible for membership in the Society and will receive this TRANSACTIONS upon payment of the annual Society membership fee of $12.00 plus an annual subscription fee of $22.00.Members of certain other professional societies are eligible to become Affiliates of the Society.For information on joining, write to the IEEE at the address below.

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 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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0380.012

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 head0.013
GPT teacher head0.231
Teacher spread0.218 · 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 designNot applicable
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".

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Citations1
Published2019
Admission routes1
Has abstractno

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