Delay‐incorporating observability and predictability analysis of safety‐critical continuous‐time systems
Bibliographic record
Abstract
The authors suggest a framework for human–automation interaction in safety‐critical continuous systems under shared control and consider continuous‐time linear time‐invariant (LTI) dynamics to formalise our physical models mathematically. Their goal is to determine whether or not a given user‐interface provides the information required for a certain task, under the assumption that the user does not have access to any information beyond what is provided in the display. They identify observability‐based conditions under which a user‐interface provides the user with necessary information to accomplish a given task, formulated as a subset of the state space. They, therefore formulate the novel delay‐incorporating user‐observable subspace, and the delay‐incorporating user‐predictable subspace and compare them with the space spanned by the combination of the states which create the task. They assume the user is a special type of observer, with capabilities corresponding to different levels of knowledge regarding the current user's input and its derivatives. In addition, they consider that state reconstruction and prediction incorporate a processing delay.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".