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Record W4281631667 · doi:10.1002/9781119986843.ch1

Relevant Past, On‐going and Future Space Missions

2022· other· en· W4281631667 on OpenAlexaboutno aff
Philippe Durand, Stéphane May

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicSynthetic Aperture Radar (SAR) Applications and Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsRemote sensingSynthetic aperture radarConstellationRadarSpace-based radarInterferometryRadar imagingComputer scienceEarth observationDisplacement (psychology)Interferometric synthetic aperture radarSpace explorationIdentification (biology)GeologyAerospace engineeringSatelliteEngineeringTelecommunicationsRadar engineering detailsPhysicsAstronomy

Abstract

fetched live from OpenAlex

To provide an overview of the images available through space agencies and their main characteristics, this chapter describes different space missions with data relevant for ground motion displacement measurements. It addresses some parameters specific to synthetic aperture radar (SAR)systems. The chapter also presents spaceborne missions that have been used or are used to compute displacement fields by interferometric processing. ERS-1 and ERS-2, the European remote sensing satellites, began the era of SAR instruments in Europe and demonstrated the first applications in radar interferometry. The Radarsat Constellation Mission aims to replace the Radarsat-2 mission, continuing the traditional Canadian C-band SAR imagery as well as adding automatic identification system payloads to improve maritime surveillance. With SAR imaging missions, free data availability, as with, for example, Sentinel-1 and future NISAR or ROSE-L missions, is of real interest for scientists in the field of displacement observations, in particular from missions with high repetitiveness and controlled orbits.

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: Other · Consensus signal: Other
Teacher disagreement score0.030
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0300.011

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 head0.204
Teacher spread0.199 · 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
GenreOther

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

Citations0
Published2022
Admission routes1
Has abstractyes

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