{"id":"W1605067897","doi":"10.1029/2007wr006054","title":"A new rainfall model based on the Neyman‐Scott process using cubic copulas","year":2008,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Copula (linguistics); Multivariate statistics; Mathematics; Marginal distribution; Poisson distribution; Spatial dependence; Duration (music); Statistical physics; Independence (probability theory); Statistics; Applied mathematics; Econometrics; Random variable","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001178465,0.0001532166,0.0001655958,0.0001421111,0.00105329,0.00005475019,0.0007339522,0.0001165209,0.004170692],"category_scores_gemma":[0.00006715494,0.00008309125,0.00009342012,0.000538648,0.0006195846,0.0001180524,0.000264223,0.0005374937,0.00334009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001134782,"about_ca_system_score_gemma":0.00003559172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001396063,"about_ca_topic_score_gemma":0.0001729889,"domain_scores_codex":[0.9971209,0.000385189,0.0001887734,0.000444261,0.001106133,0.0007547241],"domain_scores_gemma":[0.999099,0.0001248435,0.00002597834,0.0005349413,0.0000199979,0.000195204],"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":[0.0002438666,0.0001186967,0.03603957,0.0000101897,0.00002722241,0.00008921175,0.01190627,0.9343246,0.007698058,0.00001198171,0.009116688,0.0004136928],"study_design_scores_gemma":[0.0003133694,0.00008355922,0.00051908,0.00001110351,0.00001103516,0.000009898255,0.0001017921,0.9747078,0.009147734,0.001258303,0.01368172,0.0001546328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9750959,0.00001495196,0.0009919183,0.002482114,0.00001015924,0.0002301123,0.000001752148,0.00003051942,0.02114262],"genre_scores_gemma":[0.9907928,0.00000434388,0.0003679749,0.0006474172,0.00006510402,0.00002151301,0.000004583764,0.0000214899,0.00807485],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04038322,"threshold_uncertainty_score":0.9974359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07774506432816358,"score_gpt":0.3272620235802462,"score_spread":0.2495169592520826,"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."}}