The Character of Modern Technical Systems of Varying Complexity
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.
Bibliographic record
Abstract
In the modern process of making technical and technological decisions, computer design methods are increasingly applied, particularly the SolidWorks software suite, which can reasonably be regarded as a tool of artificial intelligence. The number of features and factors characterizing a technical solution and its development up to the level of a technical supersystem has become so significant that it requires local compliance with definitions, provisions, and methods of identifying the entire hierarchy of technical solutions—from a local technical solution with unregulated technical and technological connections (subsystems) to a comprehensive conglomerate of local solutions (supersystems). It should be noted that for the first time in world practice, optimization of the classification of such types of technical solutions was carried out by the modern multidisciplinary specialist Artem Aleksanyan, who possesses both the methodology of classical design and methods of computer program development. In this article, the author sets the task of linking the fundamental conclusions and definitions presented in the publications of Artem Aleksanyan with specific methodology and a system of conceptual decision-making in modern machine design involving elements of artificial intelligence and artificial neural networks.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it