MULTISTATIC SONAR OPERATOR VISUALIZATION DEVELOPMENT REQUIREMENTS
Bibliographic record
Abstract
The operational community has been using multistatic sonar for numerous years yet the development of applications for operator visualization of multistatic sonar performance is lacking. Such applications may enhance the operator’s ability to visualize the multistatic sonar's real time performance to optimize sensor employment. One of the key issues is the transition from legacy sonar monostatic performance acoustic prediction to multistatic sonar acoustic prediction. While many such applications have been developed, the majority of these pertain to the analysis of multistatic sonar and not the direct operator visualization of that performance. In order to be able to provide a completely effective system from the standpoint of both sensors and users, this challenge must be addressed. One of the key parameters is the ocean environment, in particular realistic modelling for the purpose of sonar performance prediction, yet numerous other factors will influence actual operations. Techniques have recently been field tested to provide the operator with Operational Analysis (OA) tools and advice, and further work in the area of near-real time Operator Visualization Development (OVD) is essential. A system engineering approach advocates an integrated system to provide the near-real time sonar performance prediction embedded in the sonar display. Such a system does not and will probably never fully allay the acoustic performance prediction problem given the uncertainty in acoustic analysis. However it must provide useful system performance information to the operator in order to optimize sensor employment. Hence, a new conceptual framework is required for Multistatic Sonar OVD (MSOVD).
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.013 |
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".