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Record W4322495402 · doi:10.1007/s11628-023-00528-w

Key concepts in artificial intelligence and technologies 4.0 in services

2023· article· en· W4322495402 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueService Business · 2023
Typearticle
Languageen
FieldComputer Science
TopicAI in Service Interactions
Canadian institutionsYork University
FundersDepartamento de Educación, Cultura y Deporte, Gobierno de AragónGobierno de AragónUniversidad de ZaragozaMinisterio de Ciencia, Innovación y Universidades
KeywordsKey (lock)Emerging technologiesValue (mathematics)BusinessIndustrial RevolutionService (business)Industry 4.0Human resource managementKnowledge managementMarketingComputer scienceArtificial intelligencePolitical scienceComputer security

Abstract

fetched live from OpenAlex

Abstract The emerging Industry 4.0 technologies that are impacting the global economy also represent an extraordinary opportunity to increase customer value in the service sector. Indeed, the ongoing Fourth Industrial Revolution differs from previous technologies in three main ways: (1) technological developments overcomes humans’ capabilities such that humans or even companies are no longer controlling technology; (2) customers embrace life in new technology-made environments, and (3) the boundaries between human and technology become to be blurred. This document explains these novel insights and defines the key AI-related concepts linked to each of these three distinctive aspects of Technologies 4.0 in services.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.554
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
Bibliometrics0.0010.007
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.036
GPT teacher head0.307
Teacher spread0.271 · 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