Security materials technologies roadmap
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,003 |
| Études des sciences et des technologies | 0,005 | 0,001 |
| Communication savante | 0,007 | 0,004 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,005 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,269 | 0,128 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».