Multi-Beam Echo Sounders–INS Automatic Latency Calibration
Bibliographic record
Abstract
This paper presents an automatic, rigorous, and robust method to determine a Multi-Beam Echo Sounder (MBES) and Inertial Measurement Unit/Inertial Navigation System (IMU/INS) latency calibration. The latency may be due to the IMU/INS itself, but also to the time-tagging configuration, which is generally left to the survey systems user. One survey strip over a flat seafloor is the required configuration of line for using this approach, called Multibeam IMU/INS Latency Automatic Calibration (MILAC), standing for MBES to IMU/INS Latency Automatic Calibration. The algorithm considers only data with maximum latency effects and integrates an automatic data selection for this purpose. The latency estimation procedure is based on an iterative Least Square adjustment method followed by a statistical analysis. Our process can deal with beam acoustic refraction caused by the speed of sound in sea water. The accuracy of MILAC is about 2 ms (millisecond) and its average precision of 0.15 ms. MILAC is able to determine the latency with an average resolution of 5 ms. However, the morphology of the survey line is restricted to smooth and regular seafloor and the survey platform should have a relatively high attitude rate.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".