Non-Lethal Weapons: Opportunities for R&D
Bibliographic record
Abstract
The aim of this overview study is to recommend the Non-Lethal Weapon (NLW) research and development that Defence Research and Development Canada (DRDC) could conduct over the next decade (and possibly beyond) in response to emerging defence and security NLW requirements. It summarizes the DRDC perspective of NLW technologies, which includes non-lethal applications of electro-magnetic and acoustic directed energy. The study shows that by channeling existing expertise and effort, DRDC could, over time, provide the Canadian Forces with science and technology knowledge on the effects, operational effectiveness and counter-measures of selected, emerging NLW technologies. La pr sente tude d'ensemble a pour objet de recommander les travaux de recherche et d veloppement sur les armes non l tales (ANL) que Recherche et d veloppement pour la d fense Canada (RDDC) pourrait effectuer au cours des dix prochaines ann es (et peut tre au del ) pour satisfaire aux nouveaux besoins d'ANL en vue d'assurer la d fense et la s curit . Elle r sume la perspective de RDDC sur les technologies d'ANL, notamment les applications non l tales de l' nergie lectromagn tique et acoustique dirig e. L' tude montre que si elle canalise l'expertise et le travail actuels, RDDC pourrait, au fil du temps, fournir aux Forces canadiennes des connaissances scientifiques et technologiques sur les effets, l'efficacit op rationnelle et les contre-mesures li es certaines technologies nouvelles en mati re d'ANL.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".