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Record W1949337222 · doi:10.1109/igarss.2002.1026469

Capability of Radarsat-1 for estimation of ocean surface current on the Scotian Shelf

2003· article· en· W1949337222 on OpenAlexaboutno aff
Daniel L. Hutt, J. Stockhausen, John C. Osler, D. Mosher

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsnot available
Fundersnot available
KeywordsCurrent (fluid)OceanographyRemote sensingEstimationSea surface temperatureEnvironmental scienceGeologyClimatologyMeteorologyGeographyEngineeringSystems engineering

Abstract

fetched live from OpenAlex

Doppler shifts in space-based synthetic aperture radar (SAR) data are due to movement of objects in the image area. These frequency shifts are most obvious in fast moving point targets such as ships. However, an area target such as the sea surface can also cause a measurable Doppler shift from which ocean surface currents can be estimated. We compare surface currents derived from three standard mode Radarsat-1 scenes over the Scotian Shelf to in situ currents measured with 21 self-locating datum marker buoys (SLDMBs). The SLDMBs drift with the local surface current, and their locations, obtained every 30 minutes via Argos satellite, are used to calculate the current. Three Radarsat-1 scenes were processed by Atlantis Scientific of Ottawa, Canada, to obtain the component of the surface current vector perpendicular to the path of the satellite. The results show that the noise in the derived Doppler shift was comparable to the Doppler shift expected from the relatively low surface currents prevalent on the Scotian Shelf. While it was concluded that present space-based SAR technology cannot provide accurate surface current data for Scotian Shelf conditions, the methodology and results provide a useful metric by which future SAR systems can be evaluated.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
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)0.0010.000

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 head0.039
GPT teacher head0.281
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
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
Published2003
Admission routes1
Has abstractyes

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