“Smart” Aircraft: Control in Critical Situations Created by Humans in Flight
Bibliographic record
Abstract
This article addresses safety in potentially dangerous situations created by negative manifestations of human factors during aircraft flight. These manifestations include erroneous actions by the pilot, delayed reaction to rapid changes in flight conditions, inattention, fatigue, illness, inaction, suicidal intent, hijacking of the aircraft by intruders, including terrorists, who are among the passengers, panic behavior of passengers, etc. The escalation of such a situation, considered critical, into an accident can be prevented if the aircraft is designed as a “smart” human-machine system with a high level of robotization. The automatic part of this system must be able to recognize dangerous human behavior and perform autonomous measures aimed at minimizing the risks. In the most extreme case, it must block human actions and transfer aircraft control to a fully automatic mode – until the end of the flight with a safe landing. The purpose of the article is to formulate the problem of developing an onboard automatic control system that meets such tasks, and a preliminary analysis of the possibilities of its solution. The specific features of the problem under consideration determine the choice of artificial intelligence elements, in particular neural network technology, for its effective solution.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".