Assessment of Hydrographic Data Uncertainty for Seamless Reference Surface
Bibliographic record
Abstract
The development of a seamless vertical reference surface is accompanied by a number of challenges pertinent to the availability, volume and uncertainty of bathymetric and topographic data. Data uncertainty, which is by far the most difficult to deal with, is attributed to various sources of errors including those of geodetic and hydrographic origin. The uncertainties in the geodetic measurements originate mainly from the limitations in the geodetic technique employed, i.e. terrestrial or space. Old nautical charts and topographic maps were based on terrestrial techniques, which are far less accurate than modern space techniques. In addition, the distribution of the positioning uncertainty is not expected to follow a consistent pattern across the chart (map). This is mainly due to the inconsistent datum distortion as well as the discrepancies in the measuring techniques in the subsequent chart (map) versions. The existing paper (and digitized) charts in many areas of the world were also based on old hydrographic surveying methods, for example the lead-line, which are far less accurate than modern techniques such as multibeam echo-sounding surveys. This creates inconsistent depth uncertainty across the chart. As uncertainties are propagated into the estimated transformation parameters, estimated positions and their covariance matrix, it is of utmost importance that they are properly modelled. This paper addresses the issue of uncertainty in hydrographic data and suggests ways to account for it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".