Mine Action - The Research Experience of the Royal Military Academy of Belgium
Bibliographic record
Abstract
Every day, civilians in dozens of countries around the world are injured and killed by landmines and other lethal leftovers of conflict, years after hostilities of war have ended. Once planted, a mine will never be able to tell the difference between a military and civilian footstep, and a bomblet will continue to attract children and metal dealers. In order to put an end to the suffering and casualties caused by antipersonnel mines, the Convention on the Prohibition of the Use, Stockpiling, Production and Transfer of Anti-Personnel Mines and on their Destruction (the Ottawa Convention or Mine Ban Treaty), was adopted in 1997. Further, in order to prevent suffering and casualties caused by cluster munitions at the time of their use, the Convention on the Use, Stockpiling, Production and Transfer of Cluster Munitions (the Oslo Convention), was adopted in 2008. In 1996, the Royal Military Academy (RMA) opted for the implementation of mine action technological projects funded by the Belgian Ministry of Defense and the Belgian State Secretariat for Development Cooperation. It further decided to set up a close collaboration with other Belgian universities, which started organizing their own research activities on mine action. Later, other funding sources were granted to RMA by the Belgian Science Policy, the European Commission, and the European Committee for Standardization. At a more politico-administrative level, RMA participates in the States Parties Meetings of the Mine Ban Treaty, and in this context, Prof. Acheroy created an expert group on mine action technologies with representatives of different organizations and countries, aiming at informing the States Parties of the Mine Ban Treaty about the evolution of the mine action technologies. Further, Prof. Y. Baudoin created working groups dedicated to robotics in mine action within international organization. This book reports research activities achieved by the RMA.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".