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Record W2018540269 · doi:10.5589/m02-045

Marine wind analysis from remotely sensed measurements

2002· article· en· W2018540269 on OpenAlexvenueaboutno aff
William Perrie, Ewa Dunlap, P.W. Vachon, Bechara Toulany, R.J. Anderson, Michael K. Dowd

Bibliographic record

VenueCanadian Journal of Remote Sensing · 2002
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsnot available
FundersNational Aeronautics and Space Administration
KeywordsScatterometerMesoscale meteorologyRemote sensingAltimeterMeteorologySatelliteNumerical weather predictionSynthetic aperture radarRadarWind speedEnvironmental scienceGeographyComputer science

Abstract

fetched live from OpenAlex

A primary objective of this study is to demonstrate a methodology that can potentially be used to generate high spatial resolution gridded vector wind fields for the northwest Atlantic on synoptic time scales. This task is accomplished using optimal interpolation (OI) to blend satellite wind data from European remote sensing (ERS-2) satellite scatterometers, National Aeronautics and Space Administration (NASA) scatterometers (NSCAT), and TOPEX/Poseidon and ERS-2 altimeters with numerical weather prediction (NWP) model wind estimates. The NWP model wind fields are produced using the MC2 atmospheric model (Canadian mesoscale compressible community atmospheric model). The grid for these fields is 0.25°. OI correlation functions and variances are derived from remotely sensed data. A second objective of the study is to provide a methodology for validation of synthetic aperture radar (SAR) derived vector winds from RADARSAT-1 images collected during the 1997 Labrador Sea Deep Convection Experiment (LSDCE). These winds are derived from the observed radar cross section and a scatterometer wind retrieval model. The validation of the SAR-derived winds is based on the OI winds and in situ ship-based winds.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.977
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
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.198
Teacher spread0.163 · 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 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

Citations7
Published2002
Admission routes2
Has abstractyes

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