Legal aspects of safety management systems and human factors in air traffic control
Bibliographic record
Abstract
The job of an air traffic controller is stressful by nature. Conditions like aircraft congestion in the skies, an outdated air traffic system and understaffing at control centers can add to the daily stresses of controllers and often cause fatigue. These conditions describe the current status of Air Traffic Services (ATS) in the United States. If left unaddressed, they could compromise safety, the primary objective of air traffic control. The purpose of this thesis is to assess the regulatory framework surrounding ATS specifically in the area of Safety Management Systems (SMS) and Human Factors and determine the course of action to be taken to improve safety in air traffic control. This thesis begins with a description of what air traffic controllers do and the issues that ATS face in the U.S. It then examines the regulatory framework of the International Civil Aviation Organization (ICAO) and the U.S. regarding ATS, specifically SMS and Human Factors. A comparison is made between the privatized air traffic system of Canada and the government owned air traffic system of the U.S. and how privatization makes a difference economically, politically and legally when implementing standards and regulations and enforcing them. Finally, suggestions are made in order to improve the legal framework of air traffic services in the U.S.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".