Airport Departure Flow Management (DFM): Findings from Field Trial Testing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we discuss the field testing of a Departure Flow Management (DFM) capability that has been developed by the FAA to reduce manual airport Call For Release (CFR) coordination requirements and workload, while increasing airport departure throughput and reducing delays. This field test consisted of shadow and operational phases and utilized both qualitative and quantitative methods. This study took place February and March 2008 at the Los Angeles (ZLA) Air Route Traffic Control Center (ARTCC) and Burbank (BUR), Las Vegas (LAS), Los Angeles (LAX), Ontario (ONT), and San Diego (SAN) airports. This test provided insights into how this tool changes roles and responsibilities, and how specific design features and functionality influenced the performance of the human operators. Human factors design improvements are discussed, along with the broader implications of the results of this case study for the introduction of new tools and automation into a distributed work environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it