Poly Topic Model and Control of a Friction-Enhanced Quarter Vehicle (Education of Datadriven Modelling)
Bibliographic record
Abstract
Nowadays, our students are required to use more and more mathematical tools with the help of user-friendly software. As a result, they are able to move to higher levels of abstraction in system modeling and control. One such tool is poly topic modeling, which is taught in the postgraduate advanced control course. Its mathematical description can be intimidating for students at first, but we provide them with utilities that can be used even by students who cannot or do not want to delve into the mathematical depths. We provide these students with semi-finished models, e.g. a model of a linearized quarter vehicle, which they learn in a previous course. Here, the minimum requirement is to add nonlinear friction to the simple linearized quarter vehicle model and construct a poly topic model from the resulting more compact model, and use it to apply the simple pole placement learned in the state space description of linear systems. The advantage of the poly topic modeling presented in this paper is that it can be generated directly from the measurement data of a system.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".