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Record W2160893194 · doi:10.1029/2005gl023992

Can synthetic aperture radars be used to estimate hurricane force winds?

2005· article· en· W2160893194 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

VenueGeophysical Research Letters · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsCanadian Space Agency
Fundersnot available
KeywordsSynthetic aperture radarGeologyMeteorologyRemote sensingEnvironmental sciencePhysics

Abstract

fetched live from OpenAlex

We compare wind fields retrieved from a RADARSAT‐I synthetic aperture radar (SAR) image acquired over Hurricane Ivan on September 10, 2004 using the C‐band geophysical model functions Cmod4 and its newest version Cmod5. Cmod4 has previously been shown to yield very good wind field estimates under low and moderate wind conditions. Wind directions obtained from streaks imaged by the SAR, that are well aligned with the mean surface wind direction are used to invert both algorithms to obtain estimates of the wind speed on scales of 1 km. These estimates are compared with predictions from a high‐resolution tropical cyclone model as well as local in situ data. It is found that the SAR wind speeds using Cmod5 agree reasonably well, while those from Cmod4 significantly under predict the measured wind speeds near the hurricane eye wall that reach values as high as 60 m s −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)0.000
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 score0.715
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.031
GPT teacher head0.303
Teacher spread0.272 · 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