{"id":"W1556798312","doi":"10.1109/icmla.2004.1383507","title":"Creating an SVM to play strong poker","year":2005,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; University of Alberta; National Science Foundation","keywords":"Support vector machine; Computer science; Machine learning; Artificial intelligence; Training set; Human–computer interaction","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00122774,0.0006951785,0.0006841342,0.0004592383,0.0004084448,0.0007311379,0.001122308,0.001222889,0.004482942],"category_scores_gemma":[0.003460127,0.0004869075,0.0004241448,0.0002639893,0.0004290115,0.001140636,0.0007806751,0.001391994,0.001457622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002864297,"about_ca_system_score_gemma":0.0005347843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001223004,"about_ca_topic_score_gemma":0.001404091,"domain_scores_codex":[0.9994825,0.0001126448,0.00003321257,0.0001484869,0.0001508678,0.00007225943],"domain_scores_gemma":[0.9989591,0.000425086,0.00006380034,0.0001026994,0.0003546061,0.00009471203],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004857816,0.0006477966,0.00780854,0.0001899914,0.0001745952,0.0003537626,0.0002347031,0.2795765,0.04064926,0.008298188,0.007933159,0.6536477],"study_design_scores_gemma":[0.00002208347,0.00009215392,0.0002868669,0.000005341047,0.000009314373,0.0000405953,0.00001551914,0.9924396,0.00447445,0.001360226,0.001245835,0.000008066017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07617626,0.0001002804,0.9162407,0.0003173191,0.0002381631,0.0001942202,0.00009834139,0.003307287,0.003327422],"genre_scores_gemma":[0.5111271,0.00006875631,0.4817419,0.0001560973,0.00006200245,0.0002720955,0.0002880148,0.0001959423,0.006088056],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004482942,"threshold_uncertainty_score":0.01499701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04349356215248667,"score_gpt":0.3279901815167409,"score_spread":0.2844966193642542,"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."}}