THE NEED TO IMPLEMENT THE API/PNR SYSTEM AND THE CONCEPT OF INTERVIEWING
Why this work is in the frame
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Bibliographic record
Abstract
The article reveals issues regarding the possibility of implementing the API/PNR system in Ukraine in order to ensure national security and border security of our state. Emphasizing that the basis for the future implementation of advance passenger information and passenger registration records was the signing of the Agreement between the Governments of Ukraine and the United States in 2021. According to the agreements, international airports were to be the first checkpoints where API/PNR was planned to be launched, but, unfortunately, the undeclared war by the Russian Federation and the closing of the air gates of Ukraine did not give an opportunity to start preparations for the operation of this system in time. The implementation of this system in Ukraine will ensure more effective counteraction to cross-border crime at the state border and will provide an opportunity to identify not only persons involved in illegal activities, but also persons who may be potential terrorists, illegal migrants, and drug couriers. It should be noted that the API/PNR system has proven itself positively in most developed countries of the world, such as the United States of America, Canada and most countries of the European Union. So, for example, in European countries, the functioning of this system made it possible to effectively fight and detect potentially illegal migrants, terrorists, people involved in human trafficking among flight passengers. Along with the introduction of the system of advance passenger information (API) and passenger registration records (PNR), we considered the issue of introducing and enshrining at the legislative level the concept of “interviewing”, which, in turn, would provide an additional opportunity for law enforcement agencies to counter organized crime on the state border of Ukraine. Noting the fact that Ukrainian legislation does not provide for the functioning of the API/ PNR system and does not define the body that will be empowered to work with it.
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 | 0.002 | 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 | 0.004 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| 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