An Empirical Study of Calibration in Air Traffic Control Expert Judgment
Bibliographic record
Abstract
In contrast to many studies revealing biases in the probabilistic judgments of task-naïve participants, a growing body of literature has revealed that over time, professionals are able to gain a reasonably accurate appreciation for the inherent uncertainty that exists in their work environments. The present study assessed how well experienced (working) air traffic controllers are able to predict the probability of the loss of separation between a pair of converging aircraft. Sixteen controllers expressed probabilistically whether or not the depicted pair of aircraft would lose separation. The actual probability of conflict was manipulated by varying the time differential between when each pair of aircraft would reach the point of potential conflict, coupled with uncertainty due to wind perturbations. Results revealed that in instances where perceptual information was available to distinguish between conflicts and non-conflicts, the difference between the actual conflict probability and the mean of the controllers' judged probabilities of conflict was minimal, highlighting the high calibration level of these domain experts at an aggregate level.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".