Cartas Náuticas com Modelos SEP: Evolução Histórica, e Perspectivas para Hidrografia Brasileira
Bibliographic record
Abstract
Advances in high-precision GNSS techniques have allowed the reduction of vertical uncertainties in bathymetric surveys, with a better determination of the heave, the dynamic draft and the reduction of co-tidal errors. However, it is necessary to determine a separation model (SEP) between the Chart Datum and the reference ellipsoid. This article will present a historical evolution on the development of SEP models in the world across seven countries: the United States, Canada, Netherlands, Saudi Arabia, Colombia, England and Brazil. Research from foreign countries shows uncertainties in SEP models ranging from 6.6 cm to 22.6 cm in relation to the ITRF. In the case of Brazil, a pioneering study for the SEP of Guanabara Bay is described, where an average difference of 2.5 cm was found with a standard deviation of 5.1 cm between the surface generated with the traditional reduction method and by GPS-tide. For national coverage, the Alt-Bat project is presented, which provides the use of the geoid as a vertical reference for hydrodynamic models. As for the prospects, there is a virtuous cycle for port development: investment in environmental data, provides greater accuracy of SEP models and less uncertainty of surveys, providing greater draft, possibility of increased cargo flow in ports and more resources for investment. SEP models are fundamental for the gathering of information from different vertical references, providing safe navigation and management of coastal process.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".