A multi-objective optimization approach to selecting sets of training devices
Bibliographic record
Abstract
A variety of training devices are available for preparing soldiers to employ small arms in combat. This variety exists because each type of device is better suited to some training tasks than others. All have their particular strengths and weaknesses that must be managed to deliver a comprehensive training system (Frank et al., 2000). Fielding an efficient set of training devices requires selection of the right types and quantities of training devices.In this paper, a methodology for identifying an efficient set of devices for infantry small arms training is developed. A template for describing the training requirements is created that identifies the tasks to be trained, numbers of trained personnel required, and when they are needed. The Stochastic Fleet Estimation (SaFE) model (Willick et al. 2010) is adapted to the small arms training problem and subsequently used within a multi-objective optimization risk assessment framework to select promising combinations and quantities of devices. This approach provides those acquiring and operating training devices with an analytic basis for selecting parsimonious sets of training devices while understanding the limitations of various training system options.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".