{"id":"W2056021206","doi":"10.1175/jas3735.1","title":"Predictability of Precipitation from Continental Radar Images. Part IV: Limits to Prediction","year":2006,"lang":"en","type":"article","venue":"Journal of the Atmospheric Sciences","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":123,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Foundation for Climate and Atmospheric Sciences","keywords":"Predictability; Nowcasting; Quantitative precipitation forecast; Meteorology; Radar; Climatology; Probabilistic logic; Environmental science; Precipitation; Storm; Computer science; Geology; Mathematics; Statistics; Geography; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00356353,0.0003234137,0.0003111319,0.0004154399,0.000314071,0.001261591,0.0003267805,0.0003618475,0.0006129097],"category_scores_gemma":[0.01757576,0.0002022325,0.0002799917,0.0004038648,0.001566429,0.001641184,0.0007488093,0.0006790775,0.00007159884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007044013,"about_ca_system_score_gemma":0.0004019823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003749567,"about_ca_topic_score_gemma":0.001263479,"domain_scores_codex":[0.9991602,0.0003644824,0.00005143686,0.0001487072,0.000209336,0.00006576672],"domain_scores_gemma":[0.9831657,0.01375163,0.001060376,0.001389914,0.0005082133,0.0001242392],"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":[0.001042442,0.0002336227,0.1531423,0.0002713607,0.0002028748,0.0003602711,0.0003595826,0.6672885,0.04209334,0.05544874,0.0008516621,0.07870532],"study_design_scores_gemma":[0.00002823746,0.0004748559,0.06080033,0.00003973714,0.00004749558,0.0001047795,0.0002069416,0.8854781,0.01967079,0.03235363,0.0007557202,0.00003940576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9628519,0.0008423389,0.02977953,0.0004686462,0.00001985563,0.0000363184,0.0002536138,0.00008718107,0.0056605],"genre_scores_gemma":[0.9979321,0.0001683889,0.001630448,0.00001284203,0.00001631114,0.00001379499,0.000101648,0.00000671087,0.000117802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003749567,"threshold_uncertainty_score":0.01884592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01816048546908298,"score_gpt":0.2201151479264132,"score_spread":0.2019546624573302,"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."}}