Bathymetric data integration approach to study bedforms in the estuary of the Saint‐Lawrence River
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Bibliographic record
Abstract
The identification of bedforms has an important role in the study of seafloor morphology. The presence of these dynamic structures on the seafloor represents a hazard for navigation. They also influence the hydrodynamic simulation models used in the context, for example, of coastal flooding. Generally, MultiBeam EchoSounders (MBES) are used to survey these bedforms. Unfortunately, the coverage of the MBES is limited to small areas per survey. Therefore, the analysis of large areas of interest (like navigation channels) requires the integration of different datasets acquired over overlapping areas at different times. The presence of spatial and temporal inconsistencies between these datasets may significantly affect the study of bedforms, which are subject to many natural processes (e.g., Tides; flow). This paper proposes a novel approach to integrate multisource bathymetric datasets to study bedforms. The proposed approach is based on consolidating multisource datasets and applying the Empirical Bayesian Kriging interpolation for the creation of a multisource Digital Bathymetric Model (DBM). It has been designed to be adapted for estuarine areas with a high dynamism of the seafloor, characteristic of the fluvio-marine regime of the Estuary of the Saint-Lawrence River. This area is distinguished by a high tidal cycle and the presence of fields of dunes. The study involves MBES data that was acquired daily over a field of dunes in this area over the span of 4 days for the purpose of monitoring the morphology and migration of dunes. The proposed approach performs well with a resulting surface with a reduced error relative to the original data compared to existing approaches and the conservation of the dune shape through the integration of the data sets despite the highly dynamic fluvio-marine environments.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it