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Record W2075995525 · doi:10.5194/osd-9-2885-2012

Fine-scale features on the sea surface in SAR satellite imagery – Part 1: Simultaneous in-situ measurements

2012· article· en· W2075995525 on OpenAlexafffund
Alexander Soloviev, Chris Maingot, Silvia Matt, Richard E. Dodge, Susanne Lehner, Domenico Velotto, Stephan Brusch, William Perrie, Eric M. Hochberg

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsBedford Institute of Oceanography
FundersOffice of Naval ResearchCanadian Space AgencyDeutsches Zentrum für Luft- und RaumfahrtGulf of Mexico Research Initiative
KeywordsSynthetic aperture radarRacing slickSatelliteGeologyRemote sensingSquall lineSatellite imageryIn situScale (ratio)MeteorologyClimatologyMesoscale meteorologyCartographyGeography

Abstract

fetched live from OpenAlex

Abstract. This work is aimed at identifying the origin of fine-scale features on the sea surface in synthetic aperture radar (SAR) imagery with the help of in-situ measurements as well as numerical models (presented in a companion paper). We are interested in natural and artificial features starting from the horizontal scale of the upper ocean mixed layer, around 30–50 m. These features are often associated with three-dimensional upper ocean dynamics. We have conducted a number of studies involving in-situ observations in the Straits of Florida during SAR satellite overpass. The data include examples of sharp frontal interfaces, wakes of surface ships, internal wave signatures, as well as slicks of artificial and natural origin. Atmospheric processes, such as squall lines and rain cells, produced prominent signatures on the sea surface. This data has allowed us to test an approach for distinguishing between natural and artificial features and atmospheric influences in SAR images that is based on a co-polarized phase difference filter.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.021
GPT teacher head0.230
Teacher spread0.209 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2012
Admission routes2
Has abstractyes

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