Bounding the error on bottom estimation for multi-angle swath bathymetry sonar
Bibliographic record
Abstract
With the recent introduction of multi-angle swath bathymetry (MASB) sonar to the commercial marketplace (e.g., Benthos Inc., C3D sonar, 2004), additions must be made to the current sonar lexicon. The correct interpretation of measurements made with MASB sonar, which uses filled transducer arrays to compute angle-of-arrival information (AOA) from backscattered signal, is essential not only for mapping, but for applications such as statistical bottom classification. In this paper it is shown that aside from uncorrelated channel to channel noise, there exists a tradeoff between effects that govern the error bounds on bottom estimation for surfaces having shallow grazing angle and surfaces distributed along a radial arc centered at the transducer. In the first case, as the bottom aligns with the radial direction to the receiver, footprint shift and shallow grazing angle effects dominate the uncertainty in physical bottom position (surface aligns along a single AOA). Alternatively, if signal from a radial arc arrives, a single AOA is usually estimated (not necessarily at the average location of the surface). Through theoretical treatment, simulation, and field measurements, the aforementioned factors affecting MASB bottom mapping are examined. [Work supported by NSERC.]
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".