The Influence of Errors in Visualization Systems on the Level of Safety Threat in Air Traffic
Bibliographic record
Abstract
Air traffic management is carried out by air traffic controllers assisted by complex technical systems that provide them with visualization of the traffic situation. In practice, visualization systems errors sometimes occur. The purpose of this paper is to determine the impact of errors of different types on the safety of the air traffic. The assessment of the threat level is influenced by subjective factors and cannot be expressed precisely. Therefore, the fuzzy reasoning theory has been used. The developed fuzzy model has been used to obtain a tool for simulation of the impact of various factors on traffic safety assessment. The results obtained indicate that the most important determinants of safety are the time when the air traffic controller remains unaware of the breakdown and the total time he/she does not have full knowledge of the traffic situation. It has been found that the key role for the proper operation of the air traffic visualization system and the restoration of full situational awareness is played by self-diagnostic systems that can restore the system’s correct functioning without even the controller being aware of the error occurrence. Their role in ensuring safety might be even greater than redundancy which is commonly used.
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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 | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".