{"id":"W2914772728","doi":"10.1002/cjs.11485","title":"A hierarchical point process with application to storm cell modelling","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Point processes and geometric inequalities","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia; Actua; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Point process; Storm; Inference; Cluster analysis; Hierarchical clustering; Hierarchy; Statistical inference; Computer science; Cluster (spacecraft); Hierarchical database model; Gaussian process; Point (geometry); Process (computing); Data mining; Gaussian; Statistics; Econometrics; Mathematics; Meteorology; Artificial intelligence; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.009339306,0.000700858,0.001114762,0.002126829,0.000870508,0.001493108,0.002590159,0.001700782,0.004558671],"category_scores_gemma":[0.02476946,0.0005108957,0.001983689,0.002785596,0.002412202,0.001935681,0.002796702,0.002846519,0.0005488843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001968448,"about_ca_system_score_gemma":0.001753293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.024059,"about_ca_topic_score_gemma":0.01518084,"domain_scores_codex":[0.9970328,0.001811136,0.0001050623,0.0003644629,0.0004851821,0.0002013731],"domain_scores_gemma":[0.9817013,0.01403593,0.001271149,0.0009713888,0.001575615,0.0004446224],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004915299,0.00003736154,0.004079957,0.00005838593,0.00006817595,0.00018989,0.0003515915,0.5193528,0.0003558,0.4600809,0.001360838,0.01401515],"study_design_scores_gemma":[0.000005820184,0.00001380987,0.0003075906,0.000006436591,0.000006644286,0.00001326134,0.00002141264,0.9325824,0.00004491517,0.06647789,0.0005118877,0.000007951169],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01717364,0.0001895159,0.9808508,0.0003400068,0.00003381218,0.00004559439,0.0001385045,0.00007663389,0.001151436],"genre_scores_gemma":[0.6834356,0.0008892804,0.3071355,0.0001983566,0.0002399042,0.0003827588,0.0006580531,0.0001253862,0.00693521],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.024059,"threshold_uncertainty_score":0.04939157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0258368263432248,"score_gpt":0.2598662347068148,"score_spread":0.23402940836359,"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."}}