Applications of multibeam water column imaging for hydrographic survey.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it