Analyzing the Legal Dimensions of Unmanned Combat Aerial Vehicle in the International Law
Bibliographic record
Abstract
The concept of manufacturing unmanned aircrafts developed before the Second World War. However after World War ΙΙ, the use of these aircrafts in aerial identification missions increased. In the recent decade, unmanned combat aerial vehicles used in order to identify and attack enemies in a direct manner. Unlike Classic wars, the future armed conflicts will focus on the use of military robots that have the ability to fight with robotic armaments which are 1000 kilometers distant from the battlefield and controlled by a person or function in an independent manner. According to the article 36 of the first protocol, each country should be informed about the legal principles of using different forms of robotic armaments such as unmanned combat aerial vehicles. In other words, no specific article or provision has been defined in the international law regarding the inhibition or restriction of manufacturing and developing these armaments. Although according to weapons law, these armaments don’t violate the principle of international law and they don’t threat human right. As a result any use of these unmanned combat aerial vehicles is not illegal. However, it is unlikely that using advanced remote/ robotic armaments be legal or don’t violate the principles of humanitarian law.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.036 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".