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Record W2511475293 · doi:10.1109/antem.2016.7550123

Sea ice detection from GNSS-R Delay-Doppler Map

2016· article· en· W2511475293 on OpenAlexaff
Qingyun Yan, Weimin Huang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Moisture and Remote Sensing
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSea iceDoppler effectPixelSeawaterGNSS applicationsRemote sensingSatelliteGeologyGeodesyGround truthReflectometryComputer scienceClimatologyOceanographyPhysicsArtificial intelligenceComputer vision

Abstract

fetched live from OpenAlex

In this paper, a scheme is presented for sea ice detection from Global Navigation Satellite System-Reflectometry (GNSS-R) Delay-Doppler Map (DDM). It is found that the DDM of sea ice differs from the one of seawater in the aspect of delay and Doppler spreading. This enables to distinguish sea ice from seawater through studying the number of high-value (greater than 50% of the peak power) DDM pixels. The area associated with a DDM with a number of high-value pixels less and greater than an empirical threshold (12) will be classified as covered by sea ice and seawater, respectively. The proposed method is validated by comparing the DDM-based detection results with ground-truth sea ice data and a detection accuracy of 100% is achieved.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.671
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.006

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 head0.007
GPT teacher head0.193
Teacher spread0.186 · 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 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

Citations13
Published2016
Admission routes1
Has abstractyes

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