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Record W4319073581 · doi:10.1007/978-3-031-17629-6

Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus

2023· book· en· W4319073581 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueLecture notes in mechanical engineering · 2023
Typebook
Languageen
FieldEngineering
TopicDigital Transformation in Industry
Canadian institutionsnot available
FundersNational Technical University of AthensWuhan University of TechnologyWuhan UniversityTallinna TehnikaülikoolShanghai Jiao Tong UniversityUniversità degli Studi di GenovaUniversità degli Studi di PadovaUniversidade de CoimbraConcordia UniversityUniversità di CataniaTechnische Universität KaiserslauternConsiglio Nazionale delle RicercheUniversitetet i StavangerUniversità degli Studi di CagliariIowa State UniversityTexas Tech UniversityHuazhong University of Science and TechnologyUniversity of PatrasUniversity of Hong KongUniversity of BristolOregon State UniversityUniversity of Texas at San AntonioKoç ÜniversitesiUniversidade de AveiroNational and Kapodistrian University of AthensWayne State UniversityOld Dominion University
KeywordsNexus (standard)AutomationManufacturing engineeringComputer scienceEngineeringEmbedded systemMechanical engineering

Abstract

fetched live from OpenAlex

This second volume of the proceedings of FAIM 2022 reports on cutting-edge/intelligent and sustainable strategies for next-generation manufacturing

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.969
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.251
Teacher spread0.223 · 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