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Record W4327815339 · doi:10.55092/aias20230001

Frontiers in Artificial Intelligence and Autonomous Systems

2023· article· en· W4327815339 on OpenAlexaff
Witold Pedrycz, Yingxu Wang, Imre J. Rudas, Fuchun Sun

Bibliographic record

VenueArtificial Intelligence and Autonomous Systems · 2023
Typearticle
Languageen
FieldComputer Science
TopicComputability, Logic, AI Algorithms
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsComputer scienceArtificial intelligenceCognitive sciencePsychology

Abstract

fetched live from OpenAlex

The International Journal of Artificial Intelligence and Autonomous Systems (AIAS) is a new platform for rigorous and rapid publication of the latest research findings and industrial applications in the contemporary fields of AI and autonomous systems.AIAS welcomes research articles on the theoretical, computational, cognitive, and empirical aspects of AI, autonomous systems, and their implementations.Congratulations on the publication of the inaugural issue of AIAS!On behalf of the Editorial Board, the Co-Editors-in-Chief would like to express a warm welcome to authors,

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)0.002
metaresearch head score (Gemma)0.005
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: Other · Consensus signal: Other
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.010
Scholarly communication0.0060.007
Open science0.0010.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0080.001

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 head0.061
GPT teacher head0.277
Teacher spread0.216 · 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
GenreOther

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

Citations5
Published2023
Admission routes1
Has abstractno

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