Methodologies for Analyzing the Principal Factors That Affect National Airspace System Performance
Bibliographic record
Abstract
Past studies of the National Airspace System (NAS) have typically focused on measuring and describing the characteristics of NAS performance, rather than on identifying the underlying causes. However, without a proper understanding of causal factors, even seemingly straightforward questions about NAS behavior can prove difficult to answer completely. Among the various NAS performance characteristics, our focus is on delays, an element of NAS performance that deservedly has received a great deal of attention. We discuss the differences between several key measures of delay, and three methodologies for applying these measurements to the investigation of causal factors: 1) Accounting tools, 2) Statistical models, and 3) Simulation models. While simple accounting tools and statistical models have great utility, far more insight can be gained from system-wide regression and simulation models. In particular, a simulation-based approach can give insight into the interactive effects of causal factors not likely to be identified through other techniques. We present preliminary results from each of these approaches, noting the strengths and limitations of each. An analytic toolset that includes all three of these modeling techniques offers the possibility of untangling the causes of the many complex,interconnected, and sometimes counterintuitive effects that result from changes to the NAS.
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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 | 0.020 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".