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Record W2804551007 · doi:10.2514/6.2018-2605

RADARSAT CONSTELLATION MISSION: Toward launch and operations

2018· article· en· W2804551007 on OpenAlexaffabout
Michel Doyon, Jill Smyth, Guennadi Kroupnik, Christian Carrié, Marc Sauvageau, Jean-François Lévesque, Fathelrahman Babiker, Viqar Abbasi, Christine Giguere, Stéphane Côté, Josée Bergeron

Bibliographic record

Venue2018 SpaceOps Conference · 2018
Typearticle
Languageen
FieldEngineering
TopicNuclear and radioactivity studies
Canadian institutionsCanadian Space Agency
Fundersnot available
KeywordsConstellationAeronauticsComputer scienceSystems engineeringSatellite constellationRemote sensingAerospace engineeringEngineeringGeologyPhysicsAstronomy

Abstract

fetched live from OpenAlex

objectives and operations context. The RCM is the evolution of Canada's RADARSAT Program with the objective of ensuring data continuity, improving operational use of Synthetic Aperture Radar (SAR) and improving system availability. Another benefit of the constellation lies in the distribution of the sensing capacity over multiple satellites thereby increasing the revisit frequency, especially in the North. The Mission's primary objective is to support the operational requirements of Canadian Government departments. RCM will provide greatly improved operational capability while ensuring SAR observations continue for existing users of RADARSAT-1 and RADARSAT-2 data. With emphasis on the public good applications, the RCM provides the systems and services to user departments who, in turn, deliver service to Canadians through their various mandates. The three main application areas for RCM are maritime surveillance, ecosystem monitoring and disaster management.

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)0.003
metaresearch head score (Gemma)0.002
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.946
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.005

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 head0.032
GPT teacher head0.242
Teacher spread0.211 · 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

Citations4
Published2018
Admission routes2
Has abstractyes

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Same venue2018 SpaceOps ConferenceSame topicNuclear and radioactivity studiesFrench-language works237,207