Time series difference approach for evaluating sensitivity of nonlinear dynamic systems
Why this work is in the frame
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Bibliographic record
Abstract
This research is aimed to establish a novel approach for assessing sensitivities of nonlinear systems to initial conditions and system parameters via an evaluation of Time Series Difference. An evaluation method is proposed for measuring the differences of two trajectories representing the solutions of nonlinear systems, in responding to different initial conditions and/or system parameters. Recurrence relations are established for numerically evaluating the time series differences. Various nonlinear responses are evaluated with the approach proposed. A typical nonlinear dynamic system the Duffing system are considered for demonstrating the application of the approach in numerically and graphically assessing the sensitivities. The approach shown effectiveness in the assessment and can a useful tool for scientists and engineers in evaluating the initial-condition and system-parameter dependent sensitivities.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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