The Development of Advanced Recognition Concepts for the HALIFAX Class Command and Control System TOPIC: Experimentation and Analysis
Bibliographic record
Abstract
In modern shipboard C2 systems, the traditional tactical display is being replaced by the Maritime Tactical Picture (MTP) that integrates the output of sensor sources with geographic information, contact attribute data, and the wide area picture. The recognition process, whereby input data are processed to determine a contact’s identity within a classification hierarchy, is one of the key operational processes involved in the compilation of the MTP. The current paper describes challenges encountered and processes used for the design, development and evaluation of advanced recognition concepts for the HALIFAX Class frigate, developed under Command Decision Aid Technology (COMDAT), a Defence R&D Canada Technology Demonstration Project. Aspects of the work discussed in the paper include: the development of models of the information flows and decision processes used by the HALIFAX Class operations room team in performing contact recognition tasks; an assessment of where data fusion technology might provide the most effective support to operators; automated recognition capabilities based on the truncated Dempster-Shafer fusion of attribute data from ownship and remote data sources; user
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".