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Record W3009681768 · doi:10.33832/ijca.2019.12.1.03

Cyber Physical Systems: A New Frontier of Artificial Intelligence: Summary Paper

2019· article· en· W3009681768 on OpenAlexaff
Sabah Mohammed, Jinani Fiaidh

Bibliographic record

VenueInternational Journal of Control and Automation · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Data Processing Techniques
Canadian institutionsLakehead University
Fundersnot available
KeywordsFrontierCyber-physical systemComputer scienceArtificial intelligenceEngineeringGeography

Abstract

fetched live from OpenAlex

In the last two decades we been witnessing the advancement of many applications based on the notion of embedded systems.However embedded systems have contributed only to a small-scale and standalone applications.With these systems electronic engineers design a chip board for accommodating such embedded systems following the principle of throwing enough mud at the wall and some of it will stick.Actually the recent advances in computing and communication technologies provided the wide adoption of internet-connected devices are pulling the concept of Cyber-Physical Systems (CPS) as the pervasive bridge between the real world (of people, equipment's and embedded systems) and information (the cyber world) technologies.

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)0.002
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: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.002
Scholarly communication0.0050.005
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.003

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 head0.006
GPT teacher head0.251
Teacher spread0.245 · 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
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

Citations4
Published2019
Admission routes1
Has abstractyes

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