Analysis of Departure and Arrival Profiles Using Real-Time Aircraft Data
Bibliographic record
Abstract
The quantity and rate of fuel burned during aircraft operations forms the basis of all emission inventories at airports. The international standard for calculating fuel burn and emissions produced is the landing and takeoff cycle of the International Civil Aviation Organization and forms the basis for many emission inventory models and emission charging schemes at airports. The acquisition of real-time aircraft flight data recorder information provided a unique opportunity to compare actual operational fuel flows and times in mode to the International Civil Aviation Organization standard. For departures, there is tremendous variety in fuel flow patterns, rates of fuel flow, and times in mode. Only 67% of the flights analyzed show a classic transition from takeoff to climbout. Most of the remaining flights showed essentially flat-line fuel flow profiles. All aircraft showed some fuel flow rates indicative of reduced-thrust departures. The certificated values for departure fuel burn matched favorably to the real-time totals for four-engine aircraft. However, for the twin-engine aircraft in this study, total departure fuel burn was grossly overpredicted, due to shorter observed departure times in mode. The average approach times in mode were slightly higher than the International Civil Aviation Organization norm, but approach fuel flow rates were significantly lower, yielding lower total fuel burn values. In general, total fuel burn for both departures and arrivals is overestimated by the International Civil Aviation Organization method.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".