THE NEED TO IMPLEMENT THE API/PNR SYSTEM AND THE CONCEPT OF INTERVIEWING
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.
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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 | 0.078 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".