{"id":"W2372036245","doi":"","title":"A Poisson Distribution Based Simulation for World Cup of Soccer in South Africa","year":2010,"lang":"en","type":"article","venue":"System Simulation Technology","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Champion; Poisson distribution; Ranking (information retrieval); Distribution (mathematics); Poisson regression; Quarter (Canadian coin); Computer science; Statistics; Mathematics; Simulation; Operations research; Geography; Artificial intelligence; Demography; Mathematical analysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004579672,0.0001073193,0.0003254342,0.000714472,0.00005636978,0.0000149663,0.0001241157,0.0002221126,0.00006847363],"category_scores_gemma":[0.000146628,0.0001210186,0.00008060034,0.0007087297,0.00004166518,0.0000936584,0.00001628929,0.0001365245,0.00002365695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008387702,"about_ca_system_score_gemma":0.00001773442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002221811,"about_ca_topic_score_gemma":0.00005858745,"domain_scores_codex":[0.9987764,0.000004131048,0.0007281998,0.0002597035,0.00003528379,0.000196264],"domain_scores_gemma":[0.9989336,0.00008059847,0.0005620865,0.0002985976,0.0001019097,0.00002326106],"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":[0.00005133074,0.00005137249,0.1903605,0.0001192376,0.00001180081,3.8607e-7,0.0001673044,0.5607184,0.00004908984,0.2478842,0.00001496517,0.0005714938],"study_design_scores_gemma":[0.0009543303,0.00003669886,0.01191895,0.00002625611,0.000005154605,1.131231e-7,0.00005828904,0.970597,0.0001521946,0.003907091,0.01221804,0.0001258312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6749819,0.0001469458,0.3216372,0.0002454526,0.0005712821,0.0008414946,0.0004168442,0.0001357177,0.001023154],"genre_scores_gemma":[0.9992403,4.175303e-7,0.0003879002,0.000008276565,0.00004554633,0.00004707901,0.0000876416,0.00001496117,0.0001678325],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4098787,"threshold_uncertainty_score":0.4934995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02655241375379287,"score_gpt":0.2468464635571693,"score_spread":0.2202940498033764,"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."}}