Multibeam Sonar Ray-Tracing Uncertainty Evaluation from a Hydrodynamic Model in a Highly Stratified Estuary
Bibliographic record
Abstract
The Port of Saint John is a highly stratified, hyper-tidal estuary that requires frequent hydrographic surveys to ensure that minimum under-keel clearances are maintained and to monitor dredge operations. The potential achievable accuracy and efficiency of a multibeam sonar survey correlates to the knowledge of the physical oceanographic conditions of the harbour. A high-resolution baroclinic hydrodynamic model of the Port of Saint John was constructed to understand the dynamic physical oceanographic environment over four periods representing the limits in river flow conditions. The model provides time-varying outputs of temperature and salinity at predefined depth intervals over the domain. Sound speed is derived from the model output and is used for ray-tracing multibeam sonar data and survey planning to minimize refraction errors. Ray-tracing depth error statistics are calculated from Moving Vessel Profiler (MVP) temperature and salinity casts and used to evaluate the model skill. Results demonstrate that the model can be used to replace observed sound speed profiles and stay within depth accuracy specifications within a swath width of 60 degrees, or more, for most of the year throughout the harbour. Uncertainty increases for swath widths of more than 55 degrees during the most severe stratification of the spring freshet.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".