Beyond Linear Delay Multipliers in Air Transport
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
Delays are considered one of the most important burdens of air transport, both for their social and environmental consequences and for the cost they cause for airlines and passengers. It is therefore not surprising that a large effort has been devoted to study how they propagate through the system. One of the most important indicators to assess such propagation is the delay multiplier, a ratio between outbound and inbound average delays; in spite of its widespread utilisation, its simplicity precludes capturing all details about the dynamics behind the diffusion process. Here we present a methodology that extracts a more complete relationship between the in- and outbound delays, distinguishing a linear and a nonlinear phase and thus yielding a richer description of the system’s response as a function of the delay magnitude. We validate the methodology through the study of a historical data set of flights crossing the European airspace and show how its most important airports have heterogeneous ways of reacting to extreme delays and that this reaction strongly depends on some of their global properties.
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 | 0.001 |
| 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