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Record W2097781128 · doi:10.5589/m10-020

Evaluation of new spaceborne SAR sensors for sea-ice monitoring in the Baltic Sea

2010· article· en· W2097781128 on OpenAlexvenueno aff
Leif E. B. Eriksson, Karin Borenäs, Wolfgang Dierking, Anders Berg, Maurizio Santoro, Per Pemberton, Henrik Lindh, Bengt Karlson

Bibliographic record

VenueCanadian Journal of Remote Sensing · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsRemote sensingSynthetic aperture radarSea iceBaltic seaSnowSatelliteSnow coverSea ice concentrationGeologyRadarSea ice thicknessGeographyMeteorologyCryosphereOceanographyComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

In this study, synthetic aperture radar (SAR) data from the Advanced Land Observing Satellite (ALOS) and the Envisat, RADARSAT-2, and TerraSAR-X satellites were compared to evaluate their usefulness for sea-ice monitoring in the Baltic Sea. Radar signature characteristics at different frequencies, polarizations, and spatial resolutions are presented for three examples from 2009. C-band like-polarization data, which have been used for operational sea-ice mapping since the early 1990s, serve as a reference. Advantages and disadvantages were identified for the different SAR systems and imaging modes. One conclusion is that cross-polarized data improve the discrimination between sea ice and open water. Another observation is that it is easier to identify ice ridges in L-band data than in images from shorter wavelengths. The information content of X- and C-band images is largely equivalent, whereas L-band data provide complementary information. L-band SAR also seems to be less sensitive to wet snow cover on the ice.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.931
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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)0.0000.000

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 head0.034
GPT teacher head0.258
Teacher spread0.224 · 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.

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

Citations52
Published2010
Admission routes1
Has abstractyes

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