A Convergent Approach to the Viability of the Dynamical Systems: The Cognitive Value of Complexity
Bibliographic record
Abstract
The integration of information and industrial technologies, digitalization and differentiation of sciences are accompanied by an increase in various types of complexity. This limits the capabilities of computer modelling, data mining, and predictive analytics. The increasing cognitive complexity of information flows and their diversity creates problems of safety, reliability and stability of the functioning of a complex dynamic system in extreme conditions. Here we show the possibility of cognitive visualization of signals of different nature through their geometrization in the form of a topological 3D model of functioning. Its projections are spatio-temporal signatures, the configurations of which reflect the dynamic, energetic and structural features of the model. An increase in the number of components of the signature configuration and its area under external influence indicates an increase in structural and functional complexity. Therefore, the signal structure can be analyzed in real time using complementary probabilistic and deterministic methods. A set of tools for the synthesis and analysis of 3D models has innovative potential for monitoring the functioning of elements of complex dynamic systems, risk management and predictive analytics.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".