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Record W2084412328 · doi:10.1177/1063293x04044378

Analysis and Synthesis of Reconfigurable Robotic Systems

2004· article· en· W2084412328 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

VenueConcurrent Engineering · 2004
Typearticle
Languageen
FieldEngineering
TopicFlexible and Reconfigurable Manufacturing Systems
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMass customizationRealization (probability)Key (lock)Systems engineeringComputer scienceEmbedded systemConfiguration designPersonalizationControl engineeringSystems designComputer architectureEngineeringOperating system

Abstract

fetched live from OpenAlex

Reconfigurable Manufacturing Systems (RMSs) offer a key technology for the realization of mass customization. Compared with integral systems, RMS may provide system configurations to satisfy a wide range of Functional Requirements (FRs). An important issue for RMS is configuration design, the determination of a configuration from all of feasible configurations to meet FRs. Configuration design of strongly coupled RMS, such as reconfigurable robotic system, has particular challenges due to special characteristics compared with dedicated systems. In this research, a methodology for the design of Reconfigurable Robotic Systems (RRSs) is described. The proposed methodology can be generalized to other reconfigurable manufacturing systems having similar features.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.008
GPT teacher head0.188
Teacher spread0.180 · 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