Impact of ADS-B on Controller Workload: Results from Alaska's Capstone Program
Bibliographic record
Abstract
The Capstone program introduced avionics in Alaska that included ADS-B equipment, starting in the year 2000. The program succeeded in equipping 208 aircraft in the Yukon-Kuskokwim Delta by the end of 2004, resulting in Capstone-equipped aircraft accounting for nearly 100% of part-135 operations by airplanes based in that region. This paper estimates the impact of the use of Capstone equipment on controller workload. It summarizes the results of a controller survey regarding the effect of Capstone equipment on controller tasks, and provides quantitative results regarding the effect of ADS-B equipment on controller workload. From the survey 57% of controllers indicated that they needed less time providing IFR separation services than without ADS-B, and 79% of the controllers felt that the overall efficiency of their operation had increased with ADS-B. An analysis of flight progress strips showed that the currently deployed Capstone equipment, when operating properly as required by ATC, would provide an 18% reduction in controller communications workload. The analysis also indicated that if all the aircraft in the Y-K Delta were properly equipped, the reduction in communications workload would be 26%
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".