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Record W1980250855 · doi:10.1115/msec2006-21075

A Complexity Code for Manufacturing Systems

2006· article· en· W1980250855 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFlexible and Reconfigurable Manufacturing Systems
Canadian institutionsUniversity of Windsor
FundersCanada Research Chairs
KeywordsCoding (social sciences)Computer scienceStructural complexityComputational complexity theoryProcess development execution systemCode (set theory)Computer-integrated manufacturingDistributed computingSystems engineeringEngineeringAlgorithmProgramming languageArtificial intelligenceMathematicsSet (abstract data type)

Abstract

fetched live from OpenAlex

A new coding system is introduced to classify manufacturing systems and capture the characteristics of the various pieces of equipment and the relationships among them within a manufacturing system, which contribute to their structural, time-independent inherent complexity. The manufacturing Systems Complexity Code (SCC) consists of fields representing equipment, such as machines, buffers and transporters, as well as their layout. Each field contains a string of digits, the value of which depends on the degree of structural, control, programming and operation complexity of theses entities. The developed coding system has many applications including manufacturing systems design, comparison and planning, assessment of their complexity and evaluation of redesign alternatives and trade-offs. The new coding system is described along with its applications and demonstration examples.

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 score0.630
Threshold uncertainty score0.585

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 integrity0.0000.000
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 head0.025
GPT teacher head0.217
Teacher spread0.192 · 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