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Record W2793291586 · doi:10.3390/ecrs-2-05136

The RADARSAT Constellation Mission in Support of Environmental Applications

2018· article· en· W2793291586 on OpenAlexaffabout
Mohammed Dabboor, Steve Iris, V. Singhroy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSynthetic Aperture Radar (SAR) Applications and Techniques
Canadian institutionsCanadian Space AgencyNatural Resources CanadaEnvironment and Climate Change Canada
Fundersnot available
KeywordsConstellationSynthetic aperture radarRemote sensingEarth observationSpace-based radarEnvironmental scienceComputer scienceSystems engineeringInverse synthetic aperture radarSatellite constellationRadarMeteorologyRadar imagingSatelliteGeologyGeographyRadar engineering detailsAerospace engineeringEngineeringTelecommunications

Abstract

fetched live from OpenAlex

The RADARSAT Constellation Mission (RCM) is a future Canadian spaceborne Synthetic Aperture Radar (SAR) mission, with the purpose of supporting the operational use of SAR imagery for different Earth observation applications. The mission, through its three identical satellites, will provide average daily complete coverage of Canada’s land and oceans. In this paper, we provide an overview of the RCM and its characteristics and advancements over previous Canadian SAR missions. However, emphasis is given to the expected potential of the RCM in regard to environmental applications. Experimental results of environmental applications using simulated RCM data have shown promising potential for the mission.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.332
Threshold uncertainty score0.659

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.003

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 head0.211
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations19
Published2018
Admission routes2
Has abstractyes

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