{"id":"W4408430711","doi":"10.5194/egusphere-egu25-13962","title":"Assessment of Dielectric Mixing Models for L-Band Radiometric Measurement of Liquid Water Content in Greenland Ice Sheet","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Greenland ice sheet; Radiometric dating; Mixing (physics); Dielectric; Liquid water content; Water content; Ice sheet; Content (measure theory); Environmental science; Remote sensing; Atmospheric sciences; Materials science; Geology; Physics; Oceanography; Geotechnical engineering; Optoelectronics; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009662565,0.0007612413,0.0002369936,0.0002873029,0.0002724989,0.0005522365,0.0005358773,0.0006432956,0.0002899927],"category_scores_gemma":[0.001345714,0.0002511394,0.0005020825,0.0002149259,0.00025575,0.0004592851,0.0003713407,0.0003506951,0.0001263758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001017987,"about_ca_system_score_gemma":0.000763631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03369028,"about_ca_topic_score_gemma":0.01644656,"domain_scores_codex":[0.9998515,0.0000569497,0.0000088871,0.00003374741,0.00002575048,0.00002309647],"domain_scores_gemma":[0.9994841,0.0002806829,0.00009064432,0.00003334876,0.00007904057,0.00003221108],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001453629,0.00009003487,0.01386318,0.0000234883,0.00006450994,0.00005352704,0.00004259627,0.9646041,0.008161099,0.0003623424,0.000126128,0.01246365],"study_design_scores_gemma":[0.00000896062,0.00002338531,0.002540968,0.000003355974,0.0000107937,0.000005130407,0.000008333725,0.9955583,0.001690994,0.00007135808,0.00007006864,0.000008328942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9676082,0.0001759057,0.03031846,0.0001776741,0.00001335373,0.00003043605,0.0001807888,0.0002725876,0.001222608],"genre_scores_gemma":[0.9921043,0.0000807132,0.007214827,0.00003564933,0.000006821886,0.00002531285,0.000176775,0.00002736455,0.000328166],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03369028,"threshold_uncertainty_score":0.06698835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1039573717432196,"score_gpt":0.2734232108468795,"score_spread":0.1694658391036599,"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."}}