Mobile Telephones Used as Boarding Passes: Enabling Technologies and Experimental Results
Bibliographic record
Abstract
As IATA is adopting new boarding media for airplanes, cell phones are considered as the first candidate to replace traditional tickets. This will improve the efficiency of delivery, flight time update and boarding process. This evolution is made possible by displaying two-dimensional barcodes on the mobile device screen. The boarding process involves many steps and participants before one can access the plane. First, the traveler has to check-in to his flight, get his boarding pass. Then, he/she goes through the security gate, which leads to the boarding gate. Finally, the passenger accesses the plane. At every step of the process, he/she needs to show his/her paper boarding pass, which could be replaced by a more convenient media, like cell phones. Also, more than 500 different models of cell phones are available on the North American market, each having different characteristics. These characteristics must be known in order to achieve proper barcode delivery. This paper discusses the main challenge underlying the replacement of paper with barcodes over cell phones. It also proposes an experimental base to evaluate the adaptive models needed to fit the barcodes into every screen.The present work proved that it is possible to go through the entire boarding process using a mobile device as a boarding pass, if the barcode is specifically created to fit the user's cell phone screen. The information needed to create the barcode depends on the adaptive model. Simple models require more information, which takes a long time to collect. The proposed experimentation can be used to evaluate different models.
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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 | 0.001 |
| 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 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".