Preliminary Results on Generalized Transmissibility Operators
Bibliographic record
Abstract
Transmissibility operators are mathematical objects that characterize the relationship between outputs of a dynamic system. Transmissibility operators have been used in applications including health monitoring, fault detection, fault localization, fault mitigation, output prediction, state estimation, and system identification. The transmissibility relationship can be either constructed if a model of the system is available, or estimated otherwise. The constructed or estimated transmissibility is used along with one subset of outputs to predict the response of the other subset of outputs. Transmissibility operators are usually constructed or estimated such that the dimension of the transmissibility input is equal to the dimension of the excitation signal acting on the underlying system. Numerical evidence introduced in previous papers showed that the accuracy of the predicted output improves as the number of transmissibility inputs increases. In this paper, we relax the assumption that requires the dimension of the transmissibility input to be equal to the dimension of the excitation signal acting on the underlying system, which results in a more general mathematical representation of transmissibility operators, which we call generalized transmissibility operators.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".