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Record W1932142044 · doi:10.24908/pceea.v0i0.3568

Theory of Technical Systems – Relationships to Engineering Sciences

2011· article· en· W1932142044 on OpenAlexvenueno aff
W. Ernst Eder

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldEngineering
TopicTechnology Assessment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceContext (archaeology)Engineering design processHeuristicsProcess (computing)Management scienceSystems engineeringEngineering

Abstract

fetched live from OpenAlex

Products (process or tangible systems) are defined with reference to standard ISO9000:2005. Designing products with a substantial engineering content, technical systems (TS), means to fully describe them ready for manufacture – in detail and assembly drawings, parts lists, adjustment instructions, customer instructions, etc. Designing must take into account all recognized requirements, especially implied and stated requirements for the designing-manufacturing-distributing organization, the intended transformation process (TrfP), and all other phases of the TS-life cycle, including societal norms, economic considerations, etc. The theory of technical systems (TTS) and its associated engineering design science (EDS) provides a framework for design considerations about engineering products, including the technology, TS-life cycle, properties, and other aspects of artificial transformations and their operators. The triad ‘theory - subject - method’ suggests that a systematic enginee-ring design method can be derived from TTS and EDS, to augment the usual intuitive design procedure, especially where the design problem is not routine. Engineering sciences are essential in this design process, (a) for analysis of existing systems and/or proposals for synthesis of designed systems, and (b) to assist as heuristics during design synthesis. Design synthesis is the more difficult operation, it requires iterative working. Several case examples have been published to demonstrate the application of the recommended systematic design method. Some of the relationships between engineering sciences and systematic design processes are explored to show their interdependence, and the need to provide a wider context in this area.

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)0.004
metaresearch head score (Gemma)0.006
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: Methods · Consensus signal: Methods
Teacher disagreement score0.012
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0020.014
Scholarly communication0.0060.007
Open science0.0020.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0120.004

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 head0.018
GPT teacher head0.198
Teacher spread0.181 · 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
GenreMethods

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

Citations4
Published2011
Admission routes1
Has abstractyes

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Same venueProceedings of the Canadian Engineering Education Association (CEEA)Same topicTechnology Assessment and ManagementFrench-language works237,207