Update: Concept and Operation of the Performance Data Analysis and Reporting System (PDARS)
Bibliographic record
Abstract
This paper contains a factual update to the concept and operation of the performance data analysis and reporting system (PDARS) paper originally presented at the SAE conference, Montreal Canada, 2003 by den Braven and Schade. Since 1999 the Federal Aviation Administration (FAA) has been operating a system for the collection, analysis, and reporting of performance-related data from the National Airspace System (NAS). This performance data analysis and reporting system (PDARS) has been installed at twenty Air Route Traffic Control Centers (ARTCCs), nineteen Terminal Radar Approach Control facilities (TRACONs), three service area offices, the FAA's Air Traffic Control System Command Center in Herndon, Virginia and FAA Headquarters offices in Washington, DC. The system generates and distributes close to 1000 reports daily for these facilities. PDARS calculates a range of performance measures, including traffic counts, travel times, travel distances, traffic flows, and in-trail separations. It turns these measurement data into information useful to FAA facilities through a <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> n architecture that features (1) automatic collection and analysis of radar tracks and flight plans, (2) automatic generation and distribution of daily morning reports, (3) sharing of data and reports among facilities, and (4) support for exploratory and causal analysis. PDARS applications at FAA facilities include performance measurement, route and airspace design, noise abatement analysis, traffic flow management initiative assessment and design, training, and support for search and rescue. PDARS has also been used in a range of FAA and NASA studies. Examples are the measurement of actual benefits of the Dallas/Fort Worth (DFW) Metroplex airspace, an analysis of the Los Angeles Arrival Enhancement Procedure (AEP), an analysis of the Phoenix Dryheat departure procedure, measurement of navigation accuracy of aircraft using area navigation (RNAV) en route, a study on the detection and analysis of in-close approach changes, an evaluation of the benefits of domestic reduced vertical separation minimum implementation, and a baseline study for the airspace flow program.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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".