The russian army and the border guard service of the Russian federal security service in the Arctic: countering terrorism
Bibliographic record
Abstract
The article assesses the military-political and terrorist situation in the Arctic. It is noted that in the Arctic region Russia will have to deal not only with individual countries (US, Canada, Norway and Denmark), but also with a united front of NATO states, as well as with modern challenges and security threats (illegal migration, piracy, drug trafficking, terrorism, etc.). The author analyses the activities undertaken in the Arctic by the Ministry of defence of the Russian Federation and the Border Service of the Federal Security Service of the Russian Federation to strengthen the country's defensive capabilities, to protect the borders and to counter terrorism. In accordance with the Decree of the President of the Russian Federation from 26 of December 2015, No. 664 "On Measures for Improving Government Management in the Sphere of Counteracting Terrorism", a number of operational headquarters have been set up in the country, including in Murmansk. In May 2015, at a meeting of the Federal Operational Headquarters of the National Anti-terrorism Committee, decisions were taken, aimed at developing additional measures to ensure security and protection from terrorism of facilities engaged in economic activity in the maritime space of the Russian Federation, including the Arctic.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".