Experimental Investigation of Predictive Probabilistic and Temporal Conflict Avoidance Displays
Bibliographic record
Abstract
Numerous automated systems are currently in use to assist controllers during air and naval traffic management. However, inferring the future intentions and courses of numerous aircraft or ships at various points in time remains problematic due to a variety of control disturbances. In this paper, a new graphical display concept was evaluated. This display concept provides predictive information about the time, location, and probability of potential traffic conflicts in the form of topological contour displays that were superimposed onto conventional traffic information. Performances on two formats of the new graphical display were compared to a conventional display that does not present predictive traffic information. Participants in the study engaged in a ship control collision avoidance task by performing a series of ship manoeuvres to minimize the danger levels of potential collisions. Although the results of the study are pending, it is hypothesized that the new graphical displays will assist participants in making improved manoeuvring decisions to avoid potential conflicts compared to the conventional display.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.005 | 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".