A Review of TRIZ Tools for Forecasting the Evolution of Technical Systems
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".