{"id":"W2344762371","doi":"10.1051/epjconf/201611916002","title":"On Depolarization Lidar-Based Method for The Determination of Liquid-Cloud Microphysical Properties","year":2016,"lang":"en","type":"article","venue":"EPJ Web of Conferences","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sonaca (Canada); Defence Research and Development Canada","funders":"","keywords":"Lidar; Cloud computing; Remote sensing; Depolarization; Environmental science; Liquid water; Physics; Meteorology; Geography; Computer science; Thermodynamics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001643925,0.00007897913,0.0001208489,0.000001633992,0.00006108027,0.000008423471,0.0001865211,0.00004135163,0.0002994434],"category_scores_gemma":[0.00008721487,0.00003708555,0.00005554182,0.00006616933,0.0002489599,0.00005454013,0.00002785053,0.00002516598,0.000008334026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001559127,"about_ca_system_score_gemma":0.00008120617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001129651,"about_ca_topic_score_gemma":0.00003769688,"domain_scores_codex":[0.9993514,0.00005331141,0.0001734108,0.0001354128,0.0001877518,0.00009866517],"domain_scores_gemma":[0.9993445,0.0003249153,0.0001499067,0.0001332243,0.0000272438,0.00002022615],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002918306,0.0001167854,0.005738138,0.00002133587,0.00001036813,1.232382e-7,0.0001804508,0.0001546951,0.8697088,0.00400989,0.00009043716,0.1196772],"study_design_scores_gemma":[0.0004297507,0.0009415449,0.007695596,0.00007191981,0.00002831804,2.373517e-7,0.00008849061,0.01563132,0.9723361,0.001052789,0.001612443,0.0001115274],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6479756,0.00001444953,0.3501384,0.0006506629,0.00007148587,0.0002054005,0.00000585608,0.000008030758,0.0009301915],"genre_scores_gemma":[0.9906016,0.000007089734,0.00907545,0.00006453546,0.00002759507,0.00002746147,7.804075e-7,0.000005423905,0.0001900649],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.342626,"threshold_uncertainty_score":0.3278695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01849715175432038,"score_gpt":0.2520034124977103,"score_spread":0.2335062607433899,"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."}}