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Record W1847754715 · doi:10.24908/pceea.v0i0.5853

THEORY OF TECHNICAL SYSTEMS – LEARNING TOOL FOR ENGINEERING EDUCATION

2015· article· en· W1847754715 on OpenAlexaffvenue
W. Ernst Eder

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2015
Typearticle
Languageen
FieldEngineering
TopicMechatronics Education and Applications
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsTransformation (genetics)Computer scienceOperator (biology)Systems theorySystems engineeringEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Hubka’s theory of technical systems (TTS)describes what is common to all engineering devices,whatever their physical principles. This theory is basedon a general transformation system (TrfS), which can beused to show engineering in the contexts of society,economics and historic developments. The TS-life cycleconsists of seven major TrfS, each consisting of productspecificTrfS. Each operator of a TrfS is itself a TrfS. Theconnection to the general economy, and its financialconsequences, is shown in the TS-life cycle LC4 with itssupply chain, and stages LC6 and LC6A, the operatingproduct with its supply chain and distribution chain.Transformation systems are hierarchical. Each subsystemcan be viewed as a TrfS in its own right. Each TrfSis a sub-system to a more complex system. Invention andinnovation in TrfS can be shown (historically) to alter thestate of society, beneficially and adversely. From thisTTS, Hubka derived a systematic methodology as guide todesign engineering, novel design and re-design.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.007
Scholarly communication0.0040.006
Open science0.0020.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0130.004

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 head0.209
Teacher spread0.201 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2015
Admission routes2
Has abstractyes

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Same venueProceedings of the Canadian Engineering Education Association (CEEA)Same topicMechatronics Education and ApplicationsFrench-language works237,207