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Record W2973205610 · doi:10.1515/mspe-2019-0028

A Review of TRIZ Tools for Forecasting the Evolution of Technical Systems

2019· review· en· W2973205610 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueManagement Systems in Production Engineering · 2019
Typereview
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsnot available
FundersIndependent Electricity System Operator
KeywordsTRIZBusiness managementComputer scienceManagement scienceArtificial intelligenceOperations researchEngineeringIndustrial engineeringBusiness

Abstract

fetched live from OpenAlex

Abstract This article presents tools used in the Theory of Inventive Problem Solving (TRIZ) which are useful when assessing the evolution direction of technical systems. The following matters are discussed: the S-shaped curve, laws (trends and lines) of the evolution of technical systems, multi-screen diagrams, as well as analysis of evolutionary potential. Inventive laws formulated by Gienrich Altshuller as well as laws previously formulated by a Polish writer and promoter of knowledge, Aleksander Głowacki, writing under the pen name Bolesław Prus, have been presented. Finally the innovation roadmaps have been shown. The use of individual tools has been supported by practical examples taken from research performed by the authors, and the usefulness of individual methods was evaluated. All methods have been compared and evaluated.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.817
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.098
GPT teacher head0.311
Teacher spread0.214 · 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