Artificial intelligence: what should the Canadian Army focus on?
Bibliographic record
Abstract
The Canadian Army acknowledges the importance of Artificial Intelligence in its modernization strategy. Still, it lacks focus on what functions of AI need to be pursued in the short, mid, and long-term. Focus is particularly critical to the Canadian Army as it has more limited human and financial resources to invest in research than its main ally and adversaries. Therefore, prioritization is needed for research and investments to ensure the Canadian Armed Forces and Canada's Army do not fall behind. This study examines various promising applications of artificial intelligence, reviews the Canadian Army modernization strategies, and investigates what Canada's main ally and adversaries are currently focusing on to make practical recommendations for decision-makers and staff working on modernization. Using a ranking system to predict the ability to impact the Canadian Army operations, the conclusions recommend pursuing nine specific AI-enabled applications for land warfare and offer recommendations for operationalizing artificial intelligence within the next 15 years. Ultimately, this study is relevant for any military professionals, Defence team members, and researchers interested in the potential applications of AI and ML in land warfare.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".