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Altimetry Enhanced Free-Air Gravity Anomalies in the High Latitude Region

2008· article· en· W2100738452 on OpenAlexaff
Jeong Woo Kim, D. R. Roman, Bang-Yong Lee, Yeadong Kim

Bibliographic record

VenueTerrestrial Atmospheric and Oceanic Sciences · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsUniversity of Calgary
FundersKorea Polar Research Institute
KeywordsGeodesySatellite altimetryAltimeterGeologyRemote sensingLatitudeWavelengthGravity anomalyGravitational fieldArcticOceanographyPhysicsOptics

Abstract

fetched live from OpenAlex

Available marine free-air gravity anomalies (FAGA) derived from multiple satellite altimetry missions have had geologically useful, short wavelength features removed during processing.An approach is described for augmenting these FAGA in the high latitude region with coherent higher frequency data.This added-value approach is demonstrated over the Barents Sea in the Arctic using existing FAGA predictions from the Danish National Cadastre (KMS98) as a reference.Short wavelength components between 4 and 111 km were added from reduced and correlation-filtered ERS1 168-day mission altimetry that had been sorted into ascending and descending datasets for separate processing.The processed data were then recombined by spectral quadrant swapping to generate a correlated, high frequency gravity field related to the local geologic sources.This added-value surface adjusted the reference FAGA to better reflect features at wavelengths related to the distances between altimetry tracks.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.220
Teacher spread0.188 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2008
Admission routes1
Has abstractyes

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