{"id":"W3171829021","doi":"","title":"PMNet: Improving Aerosol Predictions using Deep Neural Nets for Limited Ground Stations","year":2020,"lang":"en","type":"article","venue":"100th American Meteorological Society Annual Meeting","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Aerosol; Environmental science; Meteorology; Artificial neural network; Computer science; Remote sensing; Artificial intelligence; Geology; Geography","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005602555,0.0002819904,0.0003654875,0.00001196573,0.001136462,0.00008609235,0.0003017426,0.0001201293,0.0001003853],"category_scores_gemma":[0.001138133,0.0002586714,0.0003118448,0.0006625126,0.0007027641,0.0003381205,0.0003091781,0.0004112174,0.00001759216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001841713,"about_ca_system_score_gemma":0.00001417178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007442397,"about_ca_topic_score_gemma":0.00001127442,"domain_scores_codex":[0.9974461,0.0002063308,0.0004997524,0.0006877544,0.0003789228,0.0007811811],"domain_scores_gemma":[0.9983235,0.0006941975,0.0003877296,0.0001634079,0.00004601912,0.0003851352],"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":[0.0003615804,0.0003920126,0.09746035,0.0001208488,0.0002604087,0.00001208785,0.03321408,0.5691595,0.08514862,0.00008054992,0.002191974,0.211598],"study_design_scores_gemma":[0.0004323346,0.001082076,0.007724783,0.000009990848,0.0001007552,0.000007196249,0.01476224,0.9738069,0.000285304,0.00005559643,0.00128842,0.0004443973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8385874,0.00004024333,0.1585953,0.001616285,0.0001407571,0.0004170155,0.00008593149,0.0003197684,0.0001972719],"genre_scores_gemma":[0.8787162,0.000005495862,0.1181902,0.002540187,0.0004140407,0.00005984713,0.00002661859,0.00003243768,0.0000149574],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4046474,"threshold_uncertainty_score":0.9999865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04191484468141522,"score_gpt":0.2764202714132212,"score_spread":0.234505426731806,"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."}}