{"id":"W3111671256","doi":"10.1109/lgrs.2020.3039739","title":"Incidence Angle Dependence of Texture Statistics From Sentinel-1 HH-Polarization Images of Winter Arctic Sea Ice","year":2020,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Arctic and Antarctic ice dynamics","field":"Earth and Planetary Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"ASL Environmental Sciences (Canada); University of Victoria","funders":"Science and Engineering Research Council; Polar Knowledge Canada","keywords":"Sea ice; Synthetic aperture radar; Arctic; Remote sensing; Normalization (sociology); Geology; Artificial intelligence; Backscatter (email); Scattering; Image texture; Computer science; Climatology; Image processing; Optics; Physics; Image (mathematics); Telecommunications; Oceanography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000170813,0.0001423304,0.0002169709,0.00005238592,0.0001372482,0.00004193949,0.000181896,0.00005443573,0.00003336897],"category_scores_gemma":[0.00017547,0.0001204304,0.00003720445,0.000297055,0.000554212,0.000281154,0.00002536325,0.0001772484,0.000008446363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004369791,"about_ca_system_score_gemma":0.00005141887,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007159646,"about_ca_topic_score_gemma":0.0003044266,"domain_scores_codex":[0.9986781,0.00006797635,0.0002897841,0.0003369873,0.0003764693,0.0002507114],"domain_scores_gemma":[0.9991862,0.0002238891,0.0002269063,0.0001455126,0.00009517892,0.0001222983],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000124738,0.00001788046,0.5762168,0.0003441811,0.00004992776,0.0001692859,0.005245587,0.002419742,0.2665275,0.000009253753,0.0005876548,0.1482875],"study_design_scores_gemma":[0.0002454371,0.0000907639,0.4089785,0.0002281819,0.00006703479,0.00005308214,0.0005620602,0.5854504,0.003620518,0.000378184,0.0000499388,0.0002759107],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.724291,0.00003883706,0.273753,0.001507499,0.0001821109,0.00006221402,0.0001192428,0.00001271948,0.00003333926],"genre_scores_gemma":[0.9496976,0.00005527941,0.04714555,0.002948997,0.00008442541,4.915039e-9,0.00004110635,0.000004049284,0.00002304518],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5830306,"threshold_uncertainty_score":0.9994518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008994597767786482,"score_gpt":0.2012194854079795,"score_spread":0.192224887640193,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}