{"id":"W2994704024","doi":"10.1029/2019jd030874","title":"Meteorological Imagery for the Geostationary Lightning Mapper","year":2019,"lang":"en","type":"article","venue":"Journal of Geophysical Research Atmospheres","topic":"Lightning and Electromagnetic Phenomena","field":"Physics and Astronomy","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"National Oceanic and Atmospheric Administration; National Aeronautics and Space Administration","keywords":"Geostationary orbit; Mesoscale meteorology; Lightning (connector); Meteorology; Footprint; Remote sensing; Environmental science; Convective storm detection; Thunderstorm; Storm; Satellite imagery; Satellite; Computer science; Geography; Engineering","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":[0.0001804084,0.0003588317,0.0002021813,0.001208137,0.0002281373,0.0004005223,0.0003555405,0.0002891162,0.02062359],"category_scores_gemma":[0.0004337276,0.000147704,0.0002876351,0.001686666,0.00007787727,0.0004162621,0.0004143564,0.0005111132,0.01134958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002650238,"about_ca_system_score_gemma":0.0003823586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01052157,"about_ca_topic_score_gemma":0.01389507,"domain_scores_codex":[0.9998704,0.00001117442,0.0000115902,0.00003668497,0.00004943961,0.00002066182],"domain_scores_gemma":[0.9998063,0.00001209923,0.00002753311,0.00004027212,0.0000869925,0.00002678008],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0006000777,0.0002308945,0.03385026,0.0005490023,0.0001496191,0.0004117215,0.000458526,0.008153938,0.03358235,0.003373968,0.7119609,0.2066788],"study_design_scores_gemma":[0.0004142384,0.0000652048,0.2131165,0.0001385548,0.00006152555,0.0002036524,0.0003685994,0.03059246,0.01490743,0.002358901,0.737707,0.0000659877],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.04713497,0.0002335979,0.0132798,0.0003266603,0.0002000231,0.0004333549,0.890621,0.01008743,0.03768308],"genre_scores_gemma":[0.1253687,0.0002082312,0.05850057,0.000182531,0.0001255659,0.0004821183,0.8036388,0.001318212,0.0101753],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02062359,"threshold_uncertainty_score":0.06899279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02470179334653179,"score_gpt":0.3136227839093212,"score_spread":0.2889209905627894,"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."}}