{"id":"W4281921892","doi":"10.1364/ao.458566","title":"Polarimetric multiple scattering LiDAR model based on Poisson distribution","year":2022,"lang":"en","type":"article","venue":"Applied Optics","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Lidar; Scattering; Optics; Remote sensing; Polarimetry; Poisson distribution; Physics; Monte Carlo method; Mie scattering; Aerosol; SIGNAL (programming language); Range (aeronautics); Forward scatter; Backscatter (email); Photon counting; Atmospheric optics; Field of view; Light scattering; Photon; Materials science; Meteorology; Geology; Computer science; Statistics","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":[0.0001443886,0.0001242536,0.0001006343,0.000002294968,0.0003864488,0.000022062,0.0002217623,0.00003723639,0.0006075299],"category_scores_gemma":[0.00001573755,0.0001300247,0.00004252709,0.0003943897,0.00005214646,0.00003023885,0.0002696505,0.0001998738,0.0001430154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000353758,"about_ca_system_score_gemma":0.000011029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009107598,"about_ca_topic_score_gemma":0.000003626125,"domain_scores_codex":[0.9989161,0.00001308909,0.0001356186,0.0002736026,0.0003979272,0.0002636892],"domain_scores_gemma":[0.9995444,0.00004782195,0.00006097785,0.0002662727,0.000002374848,0.00007816758],"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":[0.00005444137,0.0001834303,0.0100152,0.000002399156,0.000003322128,0.000002492157,0.0000532443,0.9756767,0.008695709,0.000893585,0.0007388985,0.003680627],"study_design_scores_gemma":[0.0004492933,0.0001179749,0.01547898,0.000001531032,0.00001298432,0.000001054869,0.00009415664,0.9795175,0.001659691,0.0002523828,0.002181414,0.0002330186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8718462,0.000006097132,0.1138712,0.0001968958,0.00007749793,0.0002339537,0.00006769397,0.00007921467,0.01362126],"genre_scores_gemma":[0.9886892,0.000001184462,0.0103035,0.0006132947,0.00001985173,0.00004781929,0.00009022364,0.00002035109,0.0002145437],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1168431,"threshold_uncertainty_score":0.6652027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008239178666373,"score_gpt":0.1964063364274909,"score_spread":0.1881671577611179,"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."}}