The Development of Continuous Hydrographic Datum Using Geodetic Based Approaches: A Review
Bibliographic record
Abstract
Abstract The development of a continuous hydrographic datum along the coastal region for a precise datum determination for any hydrographic survey works is very important. The development started in 2005 by France that developed the first phase of Bathymetry with reference to the Ellipsoid (BATHYELLI) and its second phase in 2011. Then in 2009, the United Kingdom with collaboration with Ireland developed the Vertical Offshore Reference Frame (VORF), followed by the Continuous Chart Datum for Canadian Water (CCDCW) in 2010 by the Canadian Hydrographic Services (CHS) and Canadian Geodetic Survey (CGS). Then in 2018, Netherlands and Belgium collaborate to develop Vertical Reference Frame for the Netherlands (NEVREF) that consist of two elements which are Netherlands Quasi-Geoid 2018 (NLGEO2018) model and also Netherlands Lowest Astronomical Tide 2018 (NLLAT2018) model. Finally, the latest development of continuous hydrographic datum was conducted by the Kingdom of Saudi Arabia (KSA) in 2019, a system known as Saudi Continuous Chart Datum (SCCD). Therefore, this paper provides a review of the approaches in creating a continuous hydrographic datum which encompasses the usage of tide gauge station, satellite altimetry, and interpolation algorithm, Global Navigation Satellite System (GNSS), Digital Elevation Model (DEM), Geoid Model and Hydrodynamic Ocean Tide Models. The findings show that the integration of tidal station, satellite altimetry, GNSS levelling and geoid model is the most appropriate solution for a continuous and accurate hydrographic datum along the coastal line. Finally, the future research direction is also discussed in this review paper.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".