Human-centered systems analysis of aircraft separation from adverse weather
Bibliographic record
Abstract
Adverse weather significantly impacts the safety and efficiency of flight operations. Weather information plays a key role in mitigating the impact of adverse weather on flight operations by supporting air transportation decision-makers’ awareness of operational and mission risks. The emergence of new technologies for the surveillance, modeling, dissemination and presentation of information provides opportunities for improving both weather information and user decision-making. In order to support the development of new weather information systems, it is important to understand this complex problem thoroughly. This thesis applies a human-centered systems engineering approach to study the problem of separating aircraft from adverse weather. The approach explicitly considers the role of the human operator as part of the larger operational system. A series of models describing the interaction of the key elements of the adverse aircraft-weather encounter problem and a framework that characterizes users’ temporal decisionmaking were developed. Another framework that better matches pilots’ perspectives compared to traditional forecast verification methods articulated the value of forecast valid time according to a spacetime reference frame. The models and frameworks were validated using focused interviews with ten national subject matter experts in aviation meteorology or flight operations. The experts unanimously supported the general structure of the models and made suggestions on clarifications and refinements which were integrated in the final models. In addition, a cognitive walk-through of three adverse aircraft-weather encounters was conducted to provide an experiential perspective on the aviation weather problem. The scenarios were chosen to represent three of the most significant aviation weather hazards: icing, convective weather and low ceilings and visibility. They were built on actual meteorological information and the missions and pilot decisions were synthesized to investigate important weather encounter events. The cognitive walkthrough and the models were then used to identify opportunities for improving weather information and training. Of these, the most significant include opportunities to address users’ four-dimensional trajectorycentric perspectives and opportunities to improve the ability of pilots to make contingency plans when dealing with stochastic information.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".