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Record W2320514982 · doi:10.1080/01490419.2016.1160011

Hydrographic Vertical Separation Surfaces (HyVSEPs) for the Tidal Waters of Canada

2016· article· en· W2320514982 on OpenAlexaffabout
Catherine Robin, Shannon Nudds, Phillip MacAulay, André Godin, B. de Lange Boom, Jason Bartlett

Bibliographic record

VenueMarine Geodesy · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsCanadian Hydrographic ServiceFisheries and Oceans CanadaGeological Survey of CanadaNatural Resources Canada
Fundersnot available
KeywordsGeodetic datumHydrographyBathymetryHydrographic surveyGeoidTide gaugeGeodesySubmarine pipelineGeologyShoreSatelliteAltimeterSea-surface heightGeographyOceanographySea levelGeophysics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.871

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.189
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2016
Admission routes2
Has abstractyes

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