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Record W2766628814 · doi:10.1002/2017gl076020

Determining Coastal Mean Dynamic Topography by Geodetic Methods

2017· article· en· W2766628814 on OpenAlexafffund
Jianliang Huang

Bibliographic record

VenueGeophysical Research Letters · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsGeological Survey of CanadaNatural Resources Canada
FundersNatural Resources Canada
KeywordsGeoidGeodetic datumGeodesyRemote sensingGeologyGlobal Positioning SystemTide gaugeAltimeterSatelliteInterferometric synthetic aperture radarSynthetic aperture radarOcean surface topographySea levelComputer scienceGeophysicsOceanographyAerospace engineering

Abstract

fetched live from OpenAlex

Abstract In geodesy, coastal mean dynamic topography (MDT) was traditionally determined by spirit leveling technique. Advances in navigation satellite positioning (e.g., GPS) and geoid determination enable space‐based leveling with an accuracy of about 3 cm at tide gauges. Recent CryoSat‐2, a satellite altimetry mission with synthetic aperture radar (SAR) and SAR interferometric measurements, extends the space‐based leveling to the coastal ocean with the same accuracy. However, barriers remain in applying the two space‐based geodetic methods for MDT determination over the coastal ocean because current geoid modeling focuses primarily on land as a substitute to spirit leveling to realize the vertical datum.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.795
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.066
GPT teacher head0.371
Teacher spread0.305 · 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.

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

Citations28
Published2017
Admission routes2
Has abstractyes

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