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Record W2985534690 · doi:10.1109/igarss.2019.8898976

Radarsat Constellation Mission

2019· article· en· W2985534690 on OpenAlexaffabout
Steve Iris, Guennadi Kroupnik, Daniel De Lisle, Magdalena Wierus

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSynthetic Aperture Radar (SAR) Applications and Techniques
Canadian institutionsCanadian Space Agency
Fundersnot available
KeywordsConstellationRemote sensingSynthetic aperture radarSatelliteEarth observationCloud computingSpace-based radarComputer scienceSatellite constellationSystems engineeringCloud coverRadarEnvironmental scienceRadar imagingGeographyTelecommunicationsEngineeringRadar engineering detailsAerospace engineering

Abstract

fetched live from OpenAlex

In the 70s, several studies and airborne remote sensing experiments were conducted to propose a satellite mission concept that would best meet Canadian needs, more specifically for mapping the vast northern areas. Considering the need for imaging in darkness and cloud cover conditions an active microwave instrument as a satellite sensor has been considered as the optimal solution to respond to national requirements. Consequently, in 1980 the Government of Canada approved a new Earth observation program entitled RADARSAT [1] . Through this program, Canada has been providing without interruption C-Band Synthetic Aperture Radar (SAR) data since 1995 with the launch of RADARSAT-1 and with the introduction of RADARSAT-2 in 2007. There is also a clear commitment to maintain data continuity in the future with the current development of the next generation mission; the RADARSAT Constellation. This perennial data supply enables the users at national and international level to integrate this valuable source of information into their operational applications.

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: none
Teacher disagreement score0.965
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
Bibliometrics0.0000.000
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 head0.005
GPT teacher head0.201
Teacher spread0.196 · 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
Published2019
Admission routes2
Has abstractyes

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