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Record W2384445891

Research on MAS based CWE extraction framework

2007· article· en· W2384445891 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

VenueComputer Engineering and Applications Journal · 2007
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceOverhead (engineering)Representation (politics)MachiningDistributed computingExtraction (chemistry)EngineeringMechanical engineeringOperating systemChemistryChromatography
DOInot available

Abstract

fetched live from OpenAlex

Cutter/Workpiece Engagement (CWE) extraction is an important problem in virtual machining. B-rep model based CWE calculation has a high computational overhead especially for complicated workpieces. This paper presents a multi-agent system(MAS) for B-rep based CWE extraction that allows distributed processing. Structures of the framework and the agents are specified. An ACL compatible message structure is used for the communications between agents in the framework. A hierarchical structure is presented for the representation of geometric entities involved in CWE calculations. Finally a prototype implementation and an example are giv en to show the effectiveness and efficiency of the system.

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: Methods · Consensus signal: none
Teacher disagreement score0.883
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.300
Teacher spread0.278 · 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