Structure detection of nonlinear dynamic systems using bootstrap methods [and biomedical application]
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
Identification of NARMAX models involves estimating unknown parameters and detecting its underlying structure, which entails selecting a set of parameters to give a parsimonious description of the system. In the present study a bootstrap based structure detection algorithm is investigated. The bootstrap method is a numerical procedure for estimating parameter statistics that requires few assumptions. Its use for structure detection maintains the simplicity of routines developed for linear regression estimators but requires a less restrictive set of assumptions. The performance of this bootstrap structure detection technique was evaluated by using it to estimate the structure of a simple NARMAX model and comparing the results to those with the t-test and stepwise regression. Applicability of the method to more complex systems such as ones encountered in biomedical applications, was shown by identifying a parsimonious system description of the ankle model. Moreover, we showed that the bootstrap method yields parameter statistics that are closer to optimal than using traditional methods. The proposed method is simple to use and is robust in the presence of noise.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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