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Record W2025332060 · doi:10.1029/2007jc004413

Objective analysis of marine winds with the benefit of the Radarsat‐1 synthetic aperture radar: A nonlinear regression framework

2008· article· en· W2025332060 on OpenAlexaff
Richard E. Danielson, Michael K. Dowd, Harold Ritchie

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsEnvironment and Climate Change CanadaDalhousie University
Fundersnot available
KeywordsSynthetic aperture radarBuoyBackscatter (email)Remote sensingMeteorologyRadarWind speedEnvironmental scienceCovarianceWind directionGeologyGeodesyComputer scienceGeographyMathematicsStatisticsOceanography

Abstract

fetched live from OpenAlex

Surface wind analyses are constructed from spaceborne synthetic aperture radar (SAR) measurements along coastal regions of eastern and western North America and collocated operational marine wind forecasts. Each analysis minimizes the error sum of squares of the wind forecast, the SAR backscatter, and wind direction that is inferred from the SAR backscatter gradient. The relative importance of the SAR wind information is defined by its expected error covariances. Parameters that define these covariances are postulated for half the analyses by comparison with an independent set of buoy observations. The remaining analyses are found to compare better with buoy observations than conventional analysis approaches. It is suggested that SAR wind information generally be weighted strongly in an analysis and that an error covariance length scale of O[10 km] is appropriate.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score0.466

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.263
Teacher spread0.246 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2008
Admission routes1
Has abstractyes

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