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Record W2141710657 · doi:10.1029/2011gl047013

Mapping sea surface oil slicks using RADARSAT-2 quad-polarization SAR image

2011· article· en· W2141710657 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGeophysical Research Letters · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
Fundersnot available
KeywordsRacing slickRemote sensingSynthetic aperture radarGeologyScatteringPolarimetryEnvironmental sciencePolarization (electrochemistry)OpticsPhysics

Abstract

fetched live from OpenAlex

[1] Polarimetric SAR decomposition parameters, average alpha angle () and entropy (H) are estimated for oil-slick contaminated sea surfaces and slick-free conditions using a RADARSAT-2 quad-polarization SAR image. The values of H and within oil slick areas are significantly higher than those of the ambient sea surface, indicating the dominance of Bragg scattering for the slick-free ocean and non-Bragg scattering for the oil-slick area. In land classification, the conformity coefficient (μ) is often used to discriminate surface scattering with double-bounce or volume scattering. Based on this rationale, we also develop a method using μ as a logical scalar descriptor to map oil slicks under low-to-moderate wind conditions. The proposed method is assessed using a RADARSAT-2 quad-polarization SAR image of oil slicks in the Gulf of Mexico. Analysis shows that when μ is positive the sea surface is slick-free, whereas μ is negative for oil-slick areas. This method provides a simple and effective mapping technique for oil slick detection.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.003

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 head0.052
GPT teacher head0.293
Teacher spread0.241 · 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