STAR Localization Aids Enhancement Final Report: A Call-Up Under the Noise Monitoring Standing Offer
Bibliographic record
Abstract
Recent multistatic trials have shown that contact assessment; echo association between receivers; and localization can be complex and confusing tasks, especially in the presence of multiple contacts. Dr. Joe Maksym, a Defence R&D Canada (DRDC) scientist, developed localization algorithms for use with multistatic data that may improve operator performance. This report documents the work done to integrate existing Interactive Data Language (IDL) localization software produced by Joe Maksym into the Software Tools for Analysis and Research (STAR) suite, along with enhancements that provide a quantitative assessment of algorithm performance against real data. Some of Dr. Joe Maksym's localization algorithms were incorporated into STAR. They analytically determine the ellipse-ellipse and ellipse-bearing crossing points, which are used as inputs into a clustering routine, providing an AOP and MPP overlay onto the tactical plot. It was found that the area(s) of probability (AOP) generated by the localization algorithm could produce a clear, deterministic assessment of a contact's location that can be rapidly interpreted by both a computer and a user. This in itself is a significant advantage over relying on an individual operator's qualitative assessment of contact. These localization aids could also prove useful as an operator fixing aid, contact prioritization tool and contact classification tool. They may also be used to support higher-level data fusion and data association algorithms in a track-before-detect paradigm. Though initial results are very promising, it was clear that other versions of the localization algorithm, such as one based on ellipse-hyperbola crossing points, might provide better localizations. A number of suggestions for follow-on work are provided in this document.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.099 | 0.049 |
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".