Depth modernization by integrating mean sea surface model, ocean tide model, and precise ship positioning
Bibliographic record
Abstract
Abstract This paper presents a study on depth modernization, paralleling height modernization for land elevations. Depth modernization integrates mean sea surface (MSS) models, ocean tide models, and precise ship positioning to achieve accurate seafloor depth measurements. Conventional methods rely on tidal corrections and chart datum from temporary tide gauges, which can be challenging in regions with complex tidal patterns and inconsistent chart datums. For depth modernization, we developed (1) a hybrid MSS model using satellite altimeter data, tide gauge records, and a regional geoid model, and (2) a hydrodynamic-driven ocean model with 26 tidal constituents to determine separations between the hybrid MSS and five tidal surfaces, resulting in five ellipsoid-based surfaces analogous to a geoid model for height modernization. Precise ship positioning is demonstrated using GNSS data collected by the Legend research ship in the Pacific Ocean east of Taiwan and the Canadian spatial reference system precise point positioning toolbox. We used measurements in the Taiwan Strait to show how modern depth is implemented. Comparisons of depths in four regions from the conventional and modern methods show small (a few cm) to moderate (a few dm) differences with some variability depending on the region and equipment. Discontinuities in depths from the conventional method are analyzed. Depth modernization has significantly benefited rapid and accurate bathymetric mapping for electronic navigation charts. Future work in MSS and ocean tide models and the availability of PPP tools for depth modernization are discussed. For mapping agencies worldwide, depth modernization should be prioritized alongside height modernization to ensure rapid and accurate depth data provision.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".