{"id":"W2989217585","doi":"10.13189/ms.2019.070505","title":"Backward Simulation of Correlated Negative Binomial L'evy Process Process","year":2019,"lang":"en","type":"article","venue":"Mathematics and Statistics","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Process (computing); Negative binomial distribution; Binomial (polynomial); Statistics; Applied mathematics; Calculus (dental); Poisson distribution; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005127001,0.0001190962,0.0003004878,0.0001232986,0.00007365835,0.00008553993,0.0002175704,0.00007597268,0.0003844706],"category_scores_gemma":[0.001089262,0.00009120316,0.00002966614,0.0004377841,0.00008266831,0.0001159703,0.00003817472,0.00008600558,0.00007904173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001098202,"about_ca_system_score_gemma":0.00005367165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003801523,"about_ca_topic_score_gemma":0.000001873858,"domain_scores_codex":[0.9983082,0.00002267721,0.0006997586,0.0002443068,0.000604372,0.0001206841],"domain_scores_gemma":[0.9965107,0.001890696,0.0004771864,0.0002997225,0.000761706,0.00005998439],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001673841,0.00107645,0.02715006,0.001106917,0.0001189994,0.000003953928,0.02972437,0.3498094,0.001069108,0.5020607,0.003384606,0.08432805],"study_design_scores_gemma":[0.0002285799,0.00005541763,0.0007252865,0.0000246581,0.00001265312,9.408486e-7,0.0008125274,0.6617158,0.0005309535,0.335653,0.0001493083,0.00009084072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4232046,0.000008165281,0.5738722,0.00003850985,0.00004743435,0.0004911198,0.0001887517,0.00003219003,0.002116993],"genre_scores_gemma":[0.9492723,0.000003801247,0.05003819,0.00002345387,0.00001227487,0.00001359023,0.00001415006,0.00001247342,0.0006097829],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5260677,"threshold_uncertainty_score":0.4209683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06650162798444577,"score_gpt":0.420079863181074,"score_spread":0.3535782351966282,"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."}}