Exploratory Analysis of the Supply Concept for the Standing Contingency Task Force
Bibliographic record
Abstract
The Canadian Forces (CF) is currently undergoing major changes guided by the Chief of Defence Staff’s (CDS) vision. This vision requires both the refocusing and creation of CF capabilities. As part of this evolution the CDS envisions a Standing Contingency Task Force (SCTF) consisting of integrated maritime, aerospace and land forces that could be deployed on ten days notice to conduct an amphibious operation in an uncertain environment and have the land forces ashore supported for thirty days. This capability is relatively unknown to the CF and the concept of seabasing has yet to be analyzed using techniques available to the Canadian operations research community. The purpose of this research is to provide insight into the parameters that impact on the ability of a seabase to re-supply a landing force. This will be accomplished using a three-step process. The first step will be to investigate the methodologies used by business and military organizations to solve similar problems. Second, to discover and/or develop a tool/tools that would enable the analysis of the deployed support requirements of the Canadian Forces new concept of a rapidly deployable, integrated, expeditionary amphibious force and third to use these
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".