Bibliographic record
Abstract
For target localization, multistatic sonar systems require an adequate knowledge of both the source and receiver positions. In this paper, we use a regularized acoustic inversion method on measured direct-arrival times from several impulsive sources to track a freely drifting sonobuoy field. The shallow-water experiment involved 11 sonobuoys within a 6×8 km field, with 6 sources over approximately 70 min. Regularization allows prior information to be built into the inversion, which in this case consists of estimates (with associated uncertainties) of the source and initial sonobuoy drop positions determined from the GPS position of the aircraft at the instant of drop, as well as a model for smooth sonobuoy tracks. Closely spaced sonobuoys move along similar tracks, although there is considerable movement in different directions over the entire field (260–700 m). Positioning uncertainties are estimated using a Monte Carlo appraisal procedure to be approximately 100 m (absolute) and 65 m (relative). Submitted for the Signal Processing Young Presenter Award.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".