Hydrographic Vertical Separation Surfaces (HyVSEPs) for the Tidal Waters of Canada
Bibliographic record
Abstract
Since the advent of Global Navigation Satellite Systems, it has been possible to perform hydrographic survey reductions through the ellipsoid, which has the potential to simplify operations and improve bathymetric products. This technique requires a spatially continuous separation surface connecting chart datum (CD) to a geodetic ellipsoid. The Canadian Hydrographic Service (CHS), with support from the Canadian Geodetic Survey, has developed a new suite of such surfaces, termed Hydrographic Vertical Separations Surfaces, or HyVSEPs, for CD and seven tidal levels. They capture the spatial variability of the tidal datum and levels between tide gauges and offshore using semiempirical models coupling observations at tide stations with relative sea-level rise estimates, dynamic ocean model solutions, satellite altimetry, and a geoid model. HyVSEPs are available for all tidal waters of Canada, covering over seven million square kilometers of ocean and more than 200,000 kilometers of shoreline. This document provides an overview of the CHS's modeling approach, tools, methods, and procedures.The HyVSEP for CD defines the new hydrographic datum for the tidal waters of Canada. HyVSEPs for other tidal levels are fundamental for coastal studies, climate change adaptation and the definition of the Canadian shoreline and offshore boundaries. HyVSEPs for inland waters are not discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".