{"id":"W4233340239","doi":"10.5194/amt-2021-168","title":"Retrieving microphysical properties of concurrent pristine ice and snow using polarimetric radar observations","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"European Research Council; Office of Science; Natural Environment Research Council; Sight Research UK","keywords":"Snow; Polarimetry; Environmental science; Remote sensing; Radar; Precipitation; Meteorology; Ground truth; Atmospheric sciences; Scattering; Geology; Computer science; Geography; Physics; Optics","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":[0.0003136102,0.0004817013,0.0002749442,0.0007681838,0.0001273206,0.0004598114,0.0001860249,0.0001763275,0.0002543389],"category_scores_gemma":[0.000373404,0.0001149974,0.0003048879,0.0005541721,0.00009346597,0.0004480958,0.0002516626,0.0001885688,0.000132372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001749924,"about_ca_system_score_gemma":0.0002348952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002682085,"about_ca_topic_score_gemma":0.003568104,"domain_scores_codex":[0.9998943,0.00001488184,0.000006344446,0.00003392107,0.00003379904,0.00001674031],"domain_scores_gemma":[0.9997692,0.00005899863,0.00004963793,0.00003193519,0.00007264178,0.0000176633],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0004516942,0.0002235796,0.2093935,0.0001997713,0.000431281,0.0002538163,0.0001700342,0.2138298,0.3142498,0.0004213808,0.001363837,0.2590114],"study_design_scores_gemma":[0.000030445,0.00008939439,0.1107022,0.00001093659,0.0001027043,0.0001068037,0.00006369007,0.8460062,0.04175626,0.0002714691,0.0008337299,0.0000261597],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9557358,0.0002471063,0.04204695,0.00002756369,0.00001587697,0.00001967981,0.0007965774,0.0003130409,0.0007974645],"genre_scores_gemma":[0.9811824,0.0000925425,0.01760584,0.00001088111,0.00001838125,0.000009182439,0.0009411429,0.00001574885,0.0001239408],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002682085,"threshold_uncertainty_score":0.005332947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05325045153740746,"score_gpt":0.2424367925905252,"score_spread":0.1891863410531177,"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."}}