Materials for Sustainability in Defence: Trends, Gaps, and Opportunities in Canadian Research
Bibliographic record
Abstract
This report presents a comprehensive review of North American research in Materials for Sustainability, with a focus on Canadian contributions and their potential applications in defence. A combined literature review and scientometric analysis of over 10,000 publications from 2014 to 2024 identified six major research domains: recycling, advanced materials, advanced manufacturing, low-carbon raw material production, alternative fuels, and energy storage technologies. Canadian research shows particular strength in biocomposites, green concrete, and hydrogen-related materials. The study highlights emerging trends, research momentum, and topic interconnectivity, offering insights into how materials science can support climate mitigation and adaptation. Defence applications include lightweighting, infrastructure resilience, and low-emission energy systems, especially for Arctic environments. The report concludes with recommendations for targeted R&D to advance sustainable materials in dual-use and defence applications.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.030 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".