Bibliographic record
Abstract
This article describes methods for complying with border management agencies' requests for advance passenger information, which is creating problems for carriers in countries where such notification is mandatory. The problem is made worse because of slight differences between countries for what is required and how it is submitted. The advance information, sent to the border agencies of the country where the flight is arriving, allows the agencies to screen passengers before they land. Some agencies want to see the data for departing flights as well. Currently only the USA, Canada, Korea, Mexico, Australia and New Zealand require the reports, but it is expected to spread. A non-proprietary syntax is one goal. Today, it is text-based, but image-based elements such as biometric data will need to be scalable so they can be transmitted across multiple communications systems. The IATA Type B messaging standard for mission critical applications is well suited for current demand for the data, and a system has been developed that uses it. The system is called SITA Customs Connection. However, in the longer term airlines need to be shielded from having the responsibility for such data transmission and be able to automatically forward the data they have already gathered, without any additional processing. An airport passenger information community hub is one answer, which allows transparency and adaptability. It should be able to handle transmissions as data changes, too.
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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.014 | 0.034 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.094 | 0.122 |
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".