{"id":"W2022424129","doi":"10.1007/s10846-007-9172-7","title":"AI Optimization of a Billiard Player","year":2007,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Numerical Methods and Algorithms","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Dynamical billiards; Table (database); Computer science; Ball (mathematics); Context (archaeology); Artificial intelligence; Mathematics; Data mining; Geometry","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":[],"consensus_categories":[],"category_scores_codex":[0.00221607,0.0001248251,0.0004340316,0.0002697828,0.00003494394,0.00008106266,0.0006264681,0.00006951229,0.00001228416],"category_scores_gemma":[0.0001755614,0.00008656349,0.0001816804,0.0005187195,0.00003188348,0.0002855219,0.00006462482,0.0001875553,0.000008127497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007837562,"about_ca_system_score_gemma":0.000068621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002656414,"about_ca_topic_score_gemma":3.13161e-7,"domain_scores_codex":[0.9977716,0.0001384907,0.001158884,0.0001406476,0.0005589428,0.0002313961],"domain_scores_gemma":[0.9978352,0.0003248214,0.0008437699,0.000258571,0.0005689814,0.0001686962],"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":[0.00001975991,0.0001464185,0.0004377489,0.00005262049,0.00007986929,0.00005857626,0.000326864,0.9597845,0.0005752767,0.006561314,0.0003587878,0.03159823],"study_design_scores_gemma":[0.0003275093,0.000803044,0.0003229828,0.0005424125,0.00003874511,0.0006988257,0.0003510268,0.9831997,0.0100498,0.0008505599,0.002571417,0.000243971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003168619,0.0009411506,0.9949585,0.0003023593,0.003146055,0.0001112728,2.642921e-7,0.00001360814,0.0002099145],"genre_scores_gemma":[0.231736,0.0001142939,0.7673364,0.0001628886,0.0004772591,8.982692e-7,1.836273e-7,0.00001352876,0.0001585931],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2314191,"threshold_uncertainty_score":0.3529957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02885852018583881,"score_gpt":0.3082347174655897,"score_spread":0.2793761972797509,"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."}}