Identification and empirical characterisation of flight arrival variation and the impact on departure punctuality
Bibliographic record
Abstract
In this paper, based on field data from two years of operations at a European Airport, empirical evidence of the influence of arrival variation on departure delay flight for short scheduled ground times (i.e., up to 60 minutes) is revisited, including 'on time', 'early' and 'late' arrivals. For early and late arrivals, variation is measured together with the extension of the scheduled ground time, and the numbers of departure delay minutes recorded for the next departure are considered. Based on the results obtained, it is provided a quantitative method to identify the effects of early and late arrivals on airport operations, its magnitude is dependent of airport's dynamic factors such demand and capacity and its operational context. For the study case presented early arrivals are shown to generate higher airport operational disturbances compared with late arrivals that allow better reaction capacity to compensate with direct effects on turnaround time process.
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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".