A multi-role reconfigurable trainer for naval combat information operators
Bibliographic record
Abstract
The paper describes our efforts to identify a key set of requirements for a Naval Combat Information Operators training simulation based on a MultiRole Reconfigurable Trainer framework. The project was executed for the Royal Canadian Navy by the National Research Council Canada with the collaboration of Defence Research and Development Canada. The main training issue to be addressed is to augment team training readiness in the transition from individual training to team training. The training domain use case was the acquisition of tactical voice procedures by novice AntiSubmarine Plotting Operators (ASPO), a skill domain particularly suited for a learning-by-doing approach using training simulations. The project explored training technologies that use synthetic teammates to improve the readiness of novice individuals to participate in live team training. The approach we used to achieve this objective included a literature review of key technology areas of interests, training application prototyping, and generation of simulated data of learners’ performance. Each of these aspects of the approach is discussed in the corresponding section. The focus of the literature review was on technologies incorporating synthetic teammates using automatic speech recognition and speech synthesis to capture trainees’ utterances and simulate teammates communication. The section on the training application prototype presents a use case for the verbal tactical procedure which provided the basis to develop the software’s interactive workflows, and, in particular, the construction of a Multi-Role Reconfigurable Trainer prototype. The section on simulated data provides examples of three learning analytics methods applied to a simulated dataset for a group of trainees learning the tactical verbal procedure associated with an initial contact report. Finally, a conclusion reviews the main project results, and identifies possible areas for future research work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".