Confronting climate change: the Canadian Army and domestic operations.
Bibliographic record
Abstract
The Canadian Army has been conducting disaster relief operations in support of Canadians for over a century. With the onset of climate change and its impact on the frequency and severity of natural disasters, the Government of Canada finds itself calling upon the Canadian Armed Forces more than ever to assist Canadians in time of need. With a small army, leaders often find themselves resource-constrained given requirements to support both domestic and expeditionary operations. Preparation is key. Not only does it permit the Canadian Army to be less reactionary, it will enable a higher level of support for Canadians on the home front. Analysis of post operation reports from the past ten years indicates that efficiencies can be achieved across the Canadian Army to improve responsiveness, capabilities provided, and overall cohesion with civilian agencies. With the defense of Canada as a top priority for the Canadian Armed Forces, this study outlines simple measures that can be taken internal to the Canadian Army to better support Canadians on the home front.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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; both teacher heads agree on what is shown here.
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".