Applications of multibeam water column imaging for hydrographic survey.
Bibliographic record
Abstract
Water column imaging multibeam sonars are just now becoming widely available to the hydrographic community. Whilst originally developed to serve the fisheries community, this added functionality provides several significant advantages to the hydrographer in quality control. In order to interpret the spatial patterns of echoes within the approximately twodimensional cross-section for each ping, a complete understanding of the role of sidelobes, sectors and seabed angular response is needed. This paper reviews the imaging geometry, provides synthetic examples of the echo character of typical seafloors, and then goes on to examine real examples of mid water returns that impact on the quality of hydrographic data. Examples include interference from other sonars, propeller and engine noise, bubble wash-down, bottom detection failures, false tracking on wreck-like targets, and natural thermocline and fish targets. Each example is explained to show how, with proper interpretation, increased confidence in the validity of spurious soundings or echoes may be obtained. It is predicted that, in the near future, these data types will be routinely incorporated in the hydrographic quality control data stream. They provide both increased confidence in the sounding data quality as well as timely indicators of the imminent decline in image quality. Furthermore, the data can provide a value-added product for the fisheries and oceanographic imaging community.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".