Modelling and analysis of Canadian Forces strategic lift and pre-positioning options
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents analysis of some of the strategic lift and pre-positioning issues within the context of rapid deployability to failed and failing states conducted for the Canadian Forces (CF). A simulation framework was developed to study the effectiveness of a variety of pre-positioning options. An aircraft loading optimization model based on a genetic annealing algorithm with a novel convex hull-based measure of effectiveness was also developed to analyse different strategic lift options. The model was used both to provide insights into the optimal mix of airlift capabilities and to conduct sensitivity analysis. Historical CF deployments provided a baseline performance measure against which several movement solutions were compared and contrasted. Analysis indicates that pre-positioning of equipment and supplies at various strategic locations and use of efficient mix of transport aircraft could be potential strategies for improvement of the CF's strategic lift capability.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it