Security materials technologies roadmap
Bibliographic record
Abstract
Canada’s armoured vehicle (AV) and personal protective equipment (PPE) industry is globally competitive. The industry is based upon conventional engineered materials such as aramid fabrics (e.g., Kevlar®), polymer matrix composite laminates, and monolithic ceramics, which are rapidly approaching their performance limits. While current materials are continuing to advance, the rate of development is relatively slow compared to the rapidly evolving demands for higher performance and lower-weight protection systems. New transformational technologies are required in order to improve armour mass-efficiency (protection for a given weight) and to address capability deficiencies identified by the user communities (military, law enforcement, and first responders). The Security Materials Technologies (SMT) program, jointly lead by the National Research Council of Canada (NRC) and Defence Research and Development Canada (DRDC) and advised by an Industry Steering committee made up of Canadian Armour Industry peers, is working with industries across the value chain, from new and emerging materials to integrated armour systems, to demonstrate and transfer to Canadian industry transformational materials, structural concepts, and manufacturing technologies that will substantially improve the performance-to-weight ratio of AV and PPE protection systems. Collectively, NRC and DRDC have experience in developing high performance material solutions and multi-threat protection systems. The program can help develop improved and disruptive armour products, from concept to full-scale prototyping and evaluation, and can offer technical advice and consulting services to accelerate and substantially de-risk product development. In November 2015, NRC, DRDC and the Canadian Association of Defence and Security Industries (CADSI) hosted the Canadian Security Material Technologies Roadmap (SMTRM) workshop. The SMTRM is an industry-led strategic planning process, designed to foster development of innovative products and systems to meet future market demands. Participants from across Canada and the United States had the opportunity to learn more about the current challenges and needs of the Department of National Defence of Canada and Canadian Armed Forces, and engaged in facilitated discussions about the future market demands. The SMTRM report details the findings from the workshop. With input from the SMTRM Steering Committee, 11 technical challenges and nine advanced materials technologies solutions were identified. The workshop collected 44 project proposals, of which four overarching prioritized research areas will be explored. The selected projects support the objectives of the SMT program to better align government spending on R&D projects to the needs of industry and of future markets; and to develop highly focused partnerships, alliances, and opportunities to benefit all players. Most beneficial to the SMTRM workshop participants was the opportunity to network and have open discussions with players in the defence and security, as well as materials technologies sectors. The information shared and obtained from the workshop will pave the way to develop state-of-the-art, built-in-Canada armour technology solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.269 | 0.128 |
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".