An Observer/Predictor-Based Model of the User for Attaining Situation Awareness
Bibliographic record
Abstract
Situation awareness (SA) is essential for the safe operation of systems involving human-automation interaction. In this paper, using the theory of functional observers, we model SA for the user interacting with a continuous-time linear time-invariant dynamical system. For systems under human control or shared control, we use the proposed model to determine the required information to be displayed in the user interface for achieving SA. The user interface provides the user with the ability to observe the continuous-time outputs of the system, as well as the ability to enter continuous-time control inputs. In some systems, due to inadequacy of the displayed information, the user may not be able to accomplish the desired task. To determine the required information to be displayed and the necessary states to be tracked, we propose a model of attaining SA for the users by modeling the user as a specific type of estimator (i.e., the extended delayed functional observer/predictor). We then evaluate what information is needed for such an estimator and how the desired functional of the states have to be expanded so that the user can precisely reconstruct and accurately predict the desired task. As an application example, we investigate the problem of controlling the depth of anesthesia during surgery and determine whether there exists a feasible combination of the expanded task and the displayed information that allows the anesthetist to precisely predict the depth of anesthesia of the patient.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".