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Record W2131819360 · doi:10.2528/pierb12111509

SIMULATING GNSS-R DELAY-DOPPLER MAP OF OIL SLICKED SEA SURFACES UNDER GENERAL SCENARIOS

2013· article· en· W2131819360 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

VenueProgress In Electromagnetics Research B · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsGNSS applicationsDoppler effectComputer scienceEnvironmental scienceGeologyRemote sensingGeodesyTelecommunicationsPhysicsGlobal Positioning SystemAstronomy

Abstract

fetched live from OpenAlex

of oil slicked sea are limited to simplified scenarios which have the elevation angle of 90 ◦ (nadir reflection). In this paper, the detailed simulation process to generate GNSS-R DDMs of oil slicked sea surfaces under general scenarios is presented. The DDM of oil slicked sea surface under general scenarios are generated by combining the meansquare slope model for oil slicked/clean surfaces and the GNSS-R Zavorotny-Voronovich (Z-V) scattering model. The coordinate system transformation appropriate for general-elevation-angle scenarios are also incorporated. To validate the proposed approach, a comparison is made between the DDMs of a simplified scenario and a general scenario, which are generated based on the oil slick distribution of the Deepwater Horizon oil spill accident. Theoretical analysis reveals that oil slick may be detected within a 100 km radius coverage area around the specular point for a GNSS-R receiver under the general scenario with elevation angles of 72 ◦. 1.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.819
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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.034
GPT teacher head0.307
Teacher spread0.273 · 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