Optimal Use of Multibeam Technology in the Study of Shelf Morphodynamics
Bibliographic record
Abstract
Many of the recent advances in our understanding of sedimentary processes on the continental shelf have come about as a result of the use of multibeam sonar systems. These systems provide wide area coverage of seafloor variations in bathymetry and backscatter at typical horizontal resolutions as small as ∼ 2% of the water depth. The narrowest beam systems now provide backscatter data at resolutions approaching towed sidescan sonar while simultaneously providing co-registered, equivalent-resolution topography. Even more valuable than the static view of the seabed is an ability, through resurvey, to monitor temporal variations in the seabed. By adding the time dimension, insights can be provided into the sedimentary processes rather than just the resulting sediment distribution. To achieve this, however, requires particular attention to be placed on the limitations of these survey systems, which affect repeatable accuracy. To assess the total achievable accuracy one needs to account for all the integrated components of the survey system. In this paper, the contributions of the various sources of systematic bathymetric and backscatter error within a typical shelf multibeam survey are described. To optimize the bathymetric data, strategies for dealing with imperfections in tidal models and knowledge of the sound speed structure are described. In order to improve the backscatter data, strategies for predicting the combined effect of beam pattern residuals and the seabed angular response are detailed. To illustrate a typical result, a pair of overlapping surveys employing widely differing source sensor resolution and accuracy is combined to try to predict the relative importance of active and relict shelf morphodynamic processes.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".