{"id":"W2517564487","doi":"","title":"Multisensor, Model, and Measurement Synergy: Global Aerosol Characterization II","year":2015,"lang":"en","type":"article","venue":"2015 AGU Fall Meeting","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Aerosol; Remote sensing; Environmental science; Characterization (materials science); Computer science; Meteorology; Geography; Materials science; Nanotechnology","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":[0.001809326,0.001313413,0.0008624594,0.0006663682,0.0003487076,0.001495733,0.0005661076,0.0007243849,0.00141091],"category_scores_gemma":[0.001301511,0.0007375114,0.001312141,0.0007469231,0.0002796414,0.00170717,0.001549671,0.001033575,0.0006022918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004628575,"about_ca_system_score_gemma":0.001244566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00833971,"about_ca_topic_score_gemma":0.01105806,"domain_scores_codex":[0.9994229,0.0001464795,0.0000259179,0.0001428718,0.0001825261,0.00007924306],"domain_scores_gemma":[0.9995347,0.0001230172,0.00003819056,0.0001323312,0.0001143175,0.00005737567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001707285,0.000992902,0.05818057,0.0006076345,0.0022529,0.0004735504,0.0003900412,0.2685965,0.2315095,0.005284049,0.02321303,0.4067922],"study_design_scores_gemma":[0.0002336714,0.0004469285,0.1302996,0.00009616736,0.0007406867,0.0001906158,0.0002995452,0.7700261,0.07491178,0.004225162,0.01832348,0.0002063186],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6503409,0.00499837,0.2932349,0.003586215,0.00111188,0.0005461801,0.01007358,0.008855877,0.02725201],"genre_scores_gemma":[0.8664771,0.0008876955,0.1199795,0.0004031747,0.0002693271,0.0001290211,0.006419147,0.0008968557,0.00453811],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00833971,"threshold_uncertainty_score":0.01658231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02844016772022068,"score_gpt":0.2362093786486303,"score_spread":0.2077692109284096,"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."}}