{"id":"W4254561892","doi":"10.5194/amt-2017-50","title":"Combined retrieval of Arctic liquid water cloud and surface snow properties using airborne spectral solar remote sensing","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Deutsche Forschungsgemeinschaft","keywords":"Snow; Remote sensing; Albedo (alchemy); Arctic; Environmental science; Cloud computing; Effective radius; Sea ice; Meteorology; Materials science; Geology; Computer science; Geography; Physics; Oceanography; Astronomy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002387321,0.0004183829,0.0002641484,0.0007291989,0.0001780087,0.0004192498,0.000261078,0.0001993021,0.0005522841],"category_scores_gemma":[0.0002270864,0.0001523106,0.0003766833,0.0006746764,0.00008344789,0.0004312164,0.0002420539,0.0001486898,0.0002945176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002525334,"about_ca_system_score_gemma":0.0004765613,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0129258,"about_ca_topic_score_gemma":0.0182763,"domain_scores_codex":[0.9998736,0.00001714927,0.000006279186,0.00003840362,0.00004493967,0.00001947994],"domain_scores_gemma":[0.9998837,0.00001642252,0.00002062243,0.00001407167,0.0000538938,0.00001132668],"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":[0.0009912299,0.0003765518,0.118676,0.0004646505,0.0004783087,0.0002721568,0.0002465113,0.08067156,0.4931421,0.000660888,0.002220918,0.3017991],"study_design_scores_gemma":[0.0000856851,0.0002157771,0.2002103,0.0000414418,0.0001976386,0.0001852612,0.0001736258,0.703972,0.09182648,0.0003983589,0.002641388,0.00005201308],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9683275,0.0003654283,0.02690686,0.00003051982,0.00003863512,0.00003064374,0.001506322,0.0006395521,0.002154437],"genre_scores_gemma":[0.9709623,0.000148367,0.02651542,0.00001983949,0.00001605494,0.0000189125,0.001727083,0.00003460692,0.0005572901],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9870742,"threshold_uncertainty_score":0.02570111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02572449170808313,"score_gpt":0.2335317870627732,"score_spread":0.2078072953546901,"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."}}