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Record W2131169674 · doi:10.1109/igarss.2007.4423599

Canadian Space Agency’s Hurricane Watch Program: Archive contents, Data Access and improved planning strategies

2007· article· en· W2131169674 on OpenAlexaffabout
Sonya Banal, Steve Iris, Robert Saint-Jean

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsCanadian Space Agency
Fundersnot available
KeywordsTropical cycloneAgency (philosophy)Synthetic aperture radarVariety (cybernetics)StormMeteorologyComputer scienceSpace (punctuation)Remote sensingGeographyArtificial intelligence

Abstract

fetched live from OpenAlex

The Canadian Space Agency's Hurricane Watch program monitors tropical cyclones worldwide and acquires RADARSAT-1 Synthetic Aperture Radar imagery to provide experimental datasets to the scientific research community interested in surface wind field studies. The current HW archives spans from 1999 to 2006 and contain a variety of tropical cyclones from around the world. The images show various storm development stages, and morphological characteristics. To further promote the initiative, CSA is about to provide processed images to the scientific community through an Announcement of Opportunity. In this paper, we will demonstrate how simulations of extensive acquisition plans over the Atlantic basin can provide an improved planning strategy to increase the number of valuable images.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
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 communication0.0010.001
Open science0.0010.000
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 head0.092
GPT teacher head0.350
Teacher spread0.257 · 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 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

Citations6
Published2007
Admission routes2
Has abstractyes

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