{"id":"W139731984","doi":"","title":"Running the table: an AI for computer billiards","year":2006,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Table (database); Artificial intelligence; Task (project management); Domain (mathematical analysis); Robotics; Dynamical billiards; Competition (biology); Space (punctuation); Shot (pellet); Robot; Human–computer interaction; Mathematics; Engineering; Data mining","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":[0.0003512445,0.0001003151,0.00009091756,0.00003836434,0.0002514377,0.0003998862,0.001031841,0.00004026896,0.00002792768],"category_scores_gemma":[0.00001050269,0.00006137272,0.00004475632,0.0002633455,0.00006815373,0.0005443157,0.0001515383,0.00007598451,0.00005059191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001892146,"about_ca_system_score_gemma":0.00004493882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004176255,"about_ca_topic_score_gemma":0.0001478023,"domain_scores_codex":[0.9990211,0.00002374168,0.0002027042,0.0002906123,0.0001742136,0.0002876341],"domain_scores_gemma":[0.9991131,0.0001410749,0.00004347771,0.0005297111,0.0001341823,0.00003838891],"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":[0.000002493206,0.00005546326,0.0009244742,0.000003101748,0.000005374146,0.000001888282,0.0003031144,0.009093876,0.000205242,0.6900693,0.04414063,0.2551951],"study_design_scores_gemma":[0.00003213186,0.00007716363,0.0004066023,0.000004005663,0.000002253372,0.000004570754,0.00002813443,0.8485364,0.007583464,0.04698202,0.09621618,0.0001271103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002119661,0.0000221432,0.9896386,0.003906439,0.0005607932,0.0001454725,0.000001214475,0.0002297087,0.003375926],"genre_scores_gemma":[0.5178521,0.000001744626,0.4726417,0.004792079,0.0009707234,0.0000501594,0.00000385561,0.00001619624,0.00367143],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8394425,"threshold_uncertainty_score":0.3856112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02925777514167132,"score_gpt":0.2987649669058246,"score_spread":0.2695071917641533,"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."}}