Workforce Configuration of a Canadian Forces Gematics Division
Bibliographic record
Abstract
This paper addresses some of the pertinent issues related to the workforce configuration of a C2 organization within the Canadian Forces. The mission of the latter is to produce Geomatics Information supporting US National Imaging and Mapping Agency's (NIMA) Foundation Based Operations (FBO). Initially, the open queueing network representation of the Geomatics division (where each node or station is governed by a GI/G/s queue) is examined and its complexity analyzed. The Geomatics network belongs to the class of queueing network with signals. An alternate network architecture is proposed and the intent of which is to provide a simplified network whereby the theory of product-form solutions can be employed to evaluate the workforce configuration. The equivalence of the L norm on the waiting times between the original and the revised network is demonstrated. A nonlinear integer programming model to minimize the L norm on the waiting times for the revised network is formulated. The solution procedure involves transforming the nonlinear problem into a linear problem using approximation techniques. Fictitious data are used to illustrate the methodology.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".