{"id":"W2289436918","doi":"10.1175/bams-d-14-00173.1","title":"Multi-Radar Multi-Sensor (MRMS) Severe Weather and Aviation Products: Initial Operating Capabilities","year":2016,"lang":"en","type":"article","venue":"Bulletin of the American Meteorological Society","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":253,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tornado; Meteorology; Aviation; Environmental science; Radar; Severe weather; Suite; Quantitative precipitation estimation; Precipitation; Storm; Winter storm; Doppler radar; Weather radar; Remote sensing; Computer science; Geology; Geography; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001704398,0.0006518231,0.000266713,0.0006075942,0.0002559383,0.000932864,0.000642782,0.0003126489,0.008155647],"category_scores_gemma":[0.001878503,0.0003201667,0.0002196311,0.0004937603,0.0002536865,0.00127886,0.000563972,0.0005615234,0.003381181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005870487,"about_ca_system_score_gemma":0.0007956442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008765186,"about_ca_topic_score_gemma":0.006643133,"domain_scores_codex":[0.9995236,0.00006553709,0.0000280574,0.00006942697,0.0002418634,0.00007150626],"domain_scores_gemma":[0.9987815,0.0001744654,0.0000595157,0.0002501848,0.0005855531,0.0001487382],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.005395276,0.0008767183,0.0694922,0.0004634159,0.0001440276,0.0006452664,0.001774431,0.05604953,0.1318778,0.01025266,0.2498448,0.473184],"study_design_scores_gemma":[0.001349356,0.001085886,0.1718286,0.0001317446,0.00009519236,0.0003922559,0.0004205519,0.265085,0.1433733,0.003934747,0.4120905,0.0002129346],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.5591717,0.0009019759,0.170326,0.001532048,0.0002533013,0.002589328,0.08160239,0.09852585,0.08509739],"genre_scores_gemma":[0.7689889,0.000234348,0.1491009,0.0001928402,0.0001000494,0.0004755816,0.06474067,0.002832602,0.01333402],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.008765186,"threshold_uncertainty_score":0.02728337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02818573962043046,"score_gpt":0.2431762373353065,"score_spread":0.214990497714876,"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."}}