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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 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.
venuePublished in a venue whose home country is Canada.

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.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.209
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