Implications of Wet Leasing to Paper Airlines: Generating Capacity Outside an Air Service Agreement
Bibliographic record
Abstract
Operating aircraft under leasing has increased greatly over the past twenty years. One type of aircraft leasing, wet leasing, is the practice of providing not only the aircraft to the lessee but also at least some of the crew, and potentially the maintenance, and insurance for the aircraft. In a wet lease agreement, because the lessor usually provides the majority of the aircraft’s servicing functions, the lessor is typically considered the operator of the aircraft but the aircraft is flown under the lessee’s designator code. In states where substantial ownership regulations are less restricted, an airline in one state can own a paper airline in another state. In such a scenario, the lessor may own the foreign airline that it wet leases to and use the lessee’s traffic rights.This paper focuses on the implications of a lessor wet leasing to a foreign paper airline in which the lessor has a financial stake. One such implication includes how the lessor can use the lessee’s traffic rights to generate a route between the lessor airline’s state of registry and a third state without an air service agreement between the states. The primary, and seemingly only, example of this innovation is the TACA airlines arrangement with LACSA that created a route between El Salvador and Canada from 2006 to 2010, when El Salvador and Canada did not have an air service agreement. Other implications of similarly innovative wet leasing arrangements to a paper airline or struggling airline are also analysed, such as an expansion of capacities beyond that provided in an air services agreement (ASA).
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.032 | 0.002 |
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".