Environmental Modeling Packages for the MSTDCL TDP: Review and Recommendations (Trousses de Modelisation Environnementale Pour le PDT DCLTCM: Revue et Recommendations)
Bibliographic record
Abstract
Abstract : Since 2005 DRDC Atlantic has been conducting the Multi-Sensor Torpedo Detection, Classification, and Localization (MSTDCL) technology demonstration project aimed at improving the torpedo detection, classification, and tracking capabilities on Halifax-class frigates. This document examines the advantages to the MSTDCL project of adding a capable Environmental Analysis package for detection performance prediction. Three levels of complexity were examined: a basic level based on the Networked Underwater Warfare (NUW) developed analysis package, an intermediate level package building on the NUW package to provide improved functionality and displays while reducing operator interaction, and an Advanced Environmental Analysis package that improves the accuracy of the performance predictions by more accurately representing range-dependent environments. The advantages of each level to the MSTDCL system are compared, along with estimates of the work level required to implement the package. A low-risk approach beginning with the NUW package and advancing through the intermediate levels is recommended.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.016 |
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".