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Record W2809141157 · doi:10.1002/2017jc013148

Remote Sensing of Waves Propagating in the Marginal Ice Zone by SAR

2017· article· en· W2809141157 on OpenAlexafffund
Hui Shen, William Perrie, Yongcun Hu, Yijun He

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsBedford Institute of Oceanography
FundersOffice of Naval ResearchNatural Resources CanadaCanadian Space AgencyNational Natural Science Foundation of China
KeywordsAttenuationWavelengthGeologyRemote sensingSynthetic aperture radarWind waveSea iceWave propagationSurface waveGeophysicsMeteorologyClimatologyPhysicsOpticsOceanography

Abstract

fetched live from OpenAlex

Abstract Wave‐ice interactions are important in high sea state conditions, when waves propagate from the open ocean into the marginal ice zone (MIZ) and the pack ice. In situ observations of waves and wave‐ice interactions can be obtained at a small number of MIZ locations in costly and challenging experiments, whereas remote sensing using satellite RADARSAT‐2 SAR (synthetic aperture radar) images can observe waves throughout the MIZ, in all weather conditions. We present a methodology to retrieve MIZ wave parameters from polarimetric SAR data. As an application, we describe the characteristics of waves propagating from open water into the MIZ, as generated by a strong low pressure system that developed to the east of Greenland. As waves penetrate the MIZ, SAR remote sensing observations suggest increased dominant wavelengths, attenuated wave energy and shifted mean wave directions. The SAR observations and estimates for retrieved wave attenuation in the MIZ are shown to be consistent with wave attenuation theory and in situ field observations. Thus, valuable estimates of MIZ waves over large spatial scales at high‐resolution are provided by the SAR measurements.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.891
Threshold uncertainty score0.511

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.037
GPT teacher head0.316
Teacher spread0.279 · 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 designOther design
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
Published2017
Admission routes2
Has abstractyes

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