Disposal of surplus munitions stockpiles in Ukraine. NAMSA achievements and perspectives
Bibliographic record
Abstract
As a legacy of the Cold War, Ukraine holds more than seven million surplus small arms and light weapons and more than two million tons of excess munitions. The presence of such huge stockpiles is dangerous and represents a direct threat to the safety of the population of Ukraine as well as a potential security threat to the region. Three ammunition depots in Ukraine have suffered devastating explosions in the last few years and the situation will worsen as the stockpiles age and degrade. A 12-year NATO Partnership for Peace (PfP) project has been established at the request of Ukraine. This project was led for the first 3-years by the United States with funding from 12 other NATO Member and Partner Nations (Austria, Bulgaria, Canada, Germany, Lithuania, Luxembourg, Netherlands, Norway, Slovakia, Switzerland, Turkey, United Kingdom) and the European Union aims at the safe destruction of 1000 MANPADS, 1.5 million small arms and light weapons and 133,000 tons of munitions. The first nine months achievements of the project will be reviewed with a special emphasis on the MANPADS destruction.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".