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Record W3023320905 · doi:10.1109/jstars.2020.2991682

Multiangle, Frequency, and Polarization Radar Measurement of Ice Sheets

2020· article· en· W3023320905 on OpenAlexaff
Jie‐Bang Yan, Linfeng Li, J. Nunn, Dorthe Dahl‐Jensen, Charles O’Neill, Drew Taylor, Christopher D. Simpson, Shashank Wattal, Daniel Steinhage, Prasad Gogineni, Heinrich Miller, Olaf Eisen

Bibliographic record

VenueIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Manitoba
FundersOffice of Polar ProgramsEuropean CommissionBergens ForskningsstiftelseNational Science FoundationA.P. Møller og Hustru Chastine Mc-Kinney Møllers Fond til almene FormaalUniversitetet i BergenInstitut Polaire Français Paul Emile VictorBeijing Normal UniversityChinese Academy of SciencesStaatssekretariat für Bildung, Forschung und InnovationNational Institute of Polar ResearchSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsRemote sensingRadarGeologyPolarization (electrochemistry)Computer scienceTelecommunications

Abstract

fetched live from OpenAlex

Radio echo sounding of polar ice sheets provides important information on the ice bed topography and internal layers. These data have been used by scientists to create 3-D maps of polar ice sheets for climate modeling as well as to reconstruct the climate history that dates back to hundreds of thousands of years. In this article, we present the design and development of three surface-based multichannel radars in the VHF and UHF bands. We provide results from radar data multifrequency and polarization radar data collected over the Greenland ice sheet. All the three radars shared the same digital waveform generator and digitizer, and were installed in and operated on a tracked vehicle. The radars are operated with three different antenna arrays designed for operation over 170-230, 180-340, and 600-900 MHz. The results we obtained sounded more than 2.7 km thick ice with radars operating at frequencies as high as 850 MHz with more than 40 dB signal-to-noise ratio.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.762
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.037
GPT teacher head0.209
Teacher spread0.171 · 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 designObservational
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

Citations30
Published2020
Admission routes1
Has abstractyes

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Same venueIEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingSame topicCryospheric studies and observationsFrench-language works237,207