Bibliographic record
Abstract
This article describes some of the opportunities, as well as the potential pitfalls, in generating ancillary services to boost airline revenues without incurring large operational costs. United Airline says that baggage fees and other add-on charges for meals and seat selection will generate an additional $700 million in revenue in 2009. Usually, these ancillary services are tested on the airline’s Web site. For instance, customers may be able to pay a fee for access to the lounge. But if the added service requires operational changes, it is important to make sure the service is feasible and will not interfere with core efficiencies. It is also important to avoid alienating the upper-tier flyer by offering the same “elite” experience to anyone willing to pay an extra fee. Air Canada invented the term for un-bundled services with extra fees, “a la carte” pricing. One approach is to offer layers of service, from no-frills Economy, through Classic and Classic Plus, which are the terms developed by Frontier Airlines for its new “AirFairs” ticketing program. With a high percentage of airline customers buying tickets directly from airlines’ Web sites in the belief that the carriers’ sites offer the best deals, there is a move to add more services to them, for items such as hotels and rental cars. Deciding how to include suppliers and how many to include will affect conversion rates, especially if customers don’t find the rental company they personally prefer. Airlines are also using other booking engines and commercial sites to add new offerings without having to administer them themselves.
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.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.020 | 0.030 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.057 | 0.018 |
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".