{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003212848,0.0001114478,0.0002356936,0.00035691,0.00006371311,0.00005648327,0.0001961928,0.00004372764,0.00009202898],"category_scores_gemma":[0.0001596002,0.00009016129,0.00002405982,0.0003360929,0.00002742018,0.0001077644,0.000005740948,0.0002253704,0.00002562791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001258155,"about_ca_system_score_gemma":0.001127119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006856681,"about_ca_topic_score_gemma":0.003383777,"domain_scores_codex":[0.9989641,0.00001605318,0.0003654429,0.0001067412,0.000271691,0.0002759758],"domain_scores_gemma":[0.9982985,0.0002213991,0.0002367498,0.0001391031,0.0005877559,0.0005165191],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003805936,0.0002271928,0.006475694,0.00393132,0.0002027362,0.0004015237,0.03206364,0.08736415,0.0001056518,0.8399372,0.01770185,0.01120845],"study_design_scores_gemma":[0.00206372,0.002403998,0.000348896,0.0006038772,0.0002087407,0.0004417303,0.007564783,0.04171562,0.0008289454,0.92282,0.01999364,0.001006072],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1679917,0.000049095,0.8302744,0.0002029293,0.0000696162,0.0001646598,0.000125452,0.000004491619,0.001117704],"genre_scores_gemma":[0.8765602,0.000004261563,0.1227811,0.0001565558,0.00007898948,0.000003666539,0.000004187401,0.00002223287,0.0003887769],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7085686,"threshold_uncertainty_score":0.3676671,"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."}}