Національний центр обробки попередніх даних як елемент системи API/PNR
Bibliographic record
Abstract
The main idea of this article is to emphasize and concentrate attention on the role of the National Advance Data Processing Center in the system of implementing the Advance Passenger Information (API) and Passenger Name Record (PNR) systems (hereinafter referred to as the API/PNR systems). The functioning of this API/PNR system in Ukraine is predicted to increase the level of effectiveness of countering various types of terrorist threats, as well as other criminal threats, both on the state border of Ukraine and directly within our country. Additionally, the capabilities of Ukrainian law enforcement agencies to interact with law enforcement agencies of other world countries will be expanded, which in turn will allow identifying not only individuals who are terrorists, but also individuals who may be involved in committing other serious crimes. The main fact is that this API/PNR system has proven itself to be extremely positive in various countries of the world, such as: the United States of America, Great Britain, European Union countries (Germany, France, Romania, Hungary, Italy, Portugal and others), Albania, Mongolia, China, Canada and others [1]. In particular, the use of this system in the European Union countries has increased the number of cases of detection of illegal migrants, and in the United States of America, in addition to this category of persons, the rate of detection of persons involved in terrorist activities has increased. A separate place is occupied by the consideration of the issue of the functioning single unit of the API/PNR Passenger Information Unit system (National Preliminary Data Processing Center, hereinafter referred to as the PIU). The main tasks of this unit are to process API/PNR data in order to combat terrorism and other serious crimes. In addition, this unit will interact with both Ukrainian and foreign law enforcement agencies, namely in the context of exchanging information about passengers and will be engaged in the storage of personal data of persons crossing the state border of Ukraine. At the same time, this unit will necessarily use all available mechanisms to prevent the leakage of such data. Thus, it is necessary and at the same time timely to adopt a legislative framework for the future deployment of the API/PNR system in our country.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.016 |
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".