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Record W7100676991

RADARSAT-1 Radiometric Performance Maintained in Extended Mission

2015· article· en· W7100676991 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSynthetic Aperture Radar (SAR) Applications and Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsRadiometric calibrationCalibrationRadiometryContext (archaeology)UpgradeImage qualitySynthetic aperture radarSoftwareData processing
DOInot available

Abstract

fetched live from OpenAlex

Since its launch on November 4,1995 and the start of the routine operation on April 1, 1996, RADARSAT-1, the first Canadian SAR remote sensing satellite, provides calibrated data to worldwide users for their intended applications. Significant effort continues to be expended in the provision of radiometrically and geometrically calibrated products generated by the Canadian Data Processing Facility (CDPF). Since the end of the initial Calibration Phase in 1997, both single beams and ScanSAR are monitored routinely as part of the Maintenance Phase for radiometric performance using images of the Amazon Rainforest. In 2002, a major upgrade of the ScanSAR processor completed in CDPF made significant improvements in image quality. Since 1998, calibration measurements indicated changes in the characteristics of several previously calibrated elevation antenna patterns. Compensation for beam pattern changes is made in the processor by re-calibrating these beams. In addition, both single beams and ScanSAR are monitored routinely as part of the Maintenance Phase for image quality performance using images of RADARSAT-1 Precision Transponders (RPTs). On-board, internal calibration of the SAR instrument is also monitored. Software tools are continually developed to improve image quality operational efficiency, and new experiments are being undertaken in the context of the current extended mission. In mid 2002, due to aging considerations for the On-Board Recorder, sites within Canadian mask have been envisioned for their ability to support radiometric analyses, as a potential alternative

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)0.002
metaresearch head score (Gemma)0.003
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: Methods · Consensus signal: none
Teacher disagreement score0.191
Threshold uncertainty score0.381

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.017
GPT teacher head0.233
Teacher spread0.215 · 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
GenreMethods

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
Published2015
Admission routes1
Has abstractyes

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Same topicSynthetic Aperture Radar (SAR) Applications and TechniquesFrench-language works237,207