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ОПТИМІЗАЦІЯ ЦИФРОВИХ ДВІЙНИКІВ МОДУЛЬНИХ ВИРОБНИЧИХ СТАНЦІЙ FESTO

2025· article· uk· W4408463112 on OpenAlexaff
С.В. Мисковець, Ю.П. Фещук, М.Ю. Фещук, Ю.А. Півоварчук

Bibliographic record

VenueНаукові нотатки · 2025
Typearticle
Languageuk
FieldComputer Science
TopicCybersecurity and Information Systems
Canadian institutionsPaton (Canada)
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

В роботі розглянуті питання створення, впровадження та оптимізації цифрових двійників модульнихвиробничих станцій FESTO. Шляхом моделювання досліджено роботу програмної та апаратної частинивиробничого для центру складання блоку клапана на основі робота FESTO під управлінням PLC Siemens SIMATICS7-1500.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.051
Threshold uncertainty score0.171

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0030.005
Scholarly communication0.0090.005
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0510.017

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 head0.007
GPT teacher head0.253
Teacher spread0.246 · 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 designSimulation or modeling
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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Citations0
Published2025
Admission routes1
Has abstractyes

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