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Record W4386914893 · doi:10.36227/techrxiv.22839488.v2

Airborne investigation of quasi-specular Ku-band radar scattering for satellite altimetry over snow-covered Arctic sea ice

2023· preprint· en· W4386914893 on OpenAlexafffundabout
Jack Landy, Robbie Mallett, Rosemary Willatt, Michel Tsamados, Joshua King

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of ManitobaEnvironment and Climate Change Canada
FundersEurostarsNatural Environment Research CouncilEnvironment and Climate Change CanadaUniversity of BristolEuropean CommissionNorges ForskningsrådNational Aeronautics and Space AdministrationEuropean Space AgencyLilly EndowmentEli Lilly and CompanyNational Science Foundation
KeywordsSnowRemote sensingSatelliteAltimeterSea iceEnvironmental scienceLidarSynthetic aperture radarArcticRadar altimeterRadarMeteorologySpecular reflectionGeologyClimatologyGeographyOceanography

Abstract

fetched live from OpenAlex

Operation Icebridge (OIB) 2016 level 1b Airborne Ku-band Synthetic Aperture Radar waveforms, concurrent with both OIB lidar data and Environment and Climate Change Canada in situ data, over snow on arctic sea ice, near Eureka, Canada. Footprint-scale (~16 m × 4.5 m) snow depths are estimated and compared to in situ measurements. Relative return powers and energies of the air-snow and snow-ice interface are estimated, as well as footprint-scale roughness and slope. Quasi-specular scattering (higher return powers/energies) are observed for smoother, flatter footprints. In addition to this, satellite data is discussed, including Cryosat-2 SARIn.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.027
GPT teacher head0.239
Teacher spread0.212 · 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 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

Citations2
Published2023
Admission routes3
Has abstractyes

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