{"id":"W4401943490","doi":"10.1109/cog60054.2024.10645611","title":"Recording and Describing Poker Hands","year":2024,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Human–computer interaction; Computer graphics (images)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007863992,0.0006639969,0.0003496278,0.002479946,0.000706418,0.001957258,0.000646857,0.0007803043,0.0520764],"category_scores_gemma":[0.00917061,0.0002998901,0.0002560838,0.001984146,0.0006521145,0.002872763,0.001915597,0.0007275548,0.01588711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004028583,"about_ca_system_score_gemma":0.000654102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002051316,"about_ca_topic_score_gemma":0.003348237,"domain_scores_codex":[0.9990988,0.000193331,0.000118745,0.0001954917,0.0003114901,0.0000822348],"domain_scores_gemma":[0.996718,0.001650902,0.0001976626,0.0007440008,0.0005482735,0.0001411144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001163736,0.000109446,0.008828609,0.001679581,0.00003365698,0.001437078,0.006081935,0.004254852,0.01949213,0.04285026,0.1867408,0.7273279],"study_design_scores_gemma":[0.00005554884,0.0001403071,0.01227898,0.0007759639,0.00002237938,0.001487182,0.002161216,0.008803134,0.02514987,0.01870505,0.9302647,0.0001556534],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1082454,0.002626176,0.4567962,0.0009385343,0.002081133,0.001828827,0.1226839,0.03366725,0.2711326],"genre_scores_gemma":[0.4194715,0.002329612,0.3057593,0.000762512,0.0006066904,0.002912518,0.1065338,0.009949137,0.1516749],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0520764,"threshold_uncertainty_score":0.1742129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07328073368430434,"score_gpt":0.2921461780954834,"score_spread":0.2188654444111791,"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."}}