{"id":"W4389315168","doi":"10.1109/cog57401.2023.10333165","title":"Deep Dive on Checkers Endgame Data","year":2023,"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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Chess endgame; Computer science; Artificial intelligence; Artificial neural network; Machine learning; Transfer of learning; Set (abstract data type); Sample (material)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009733513,0.001201991,0.0005571523,0.001693192,0.000652517,0.0008720111,0.001599506,0.001472309,0.007286624],"category_scores_gemma":[0.004767205,0.0003552526,0.0008155594,0.001306519,0.0006512797,0.001052543,0.001558822,0.002296381,0.005704366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001015642,"about_ca_system_score_gemma":0.001001232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03022126,"about_ca_topic_score_gemma":0.06859848,"domain_scores_codex":[0.9993023,0.00009499898,0.00003599124,0.0002138627,0.0002392528,0.0001136836],"domain_scores_gemma":[0.9989378,0.0002547539,0.00006250428,0.000235835,0.0003551742,0.0001538325],"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":[0.002470801,0.003078525,0.05139793,0.000860757,0.0004114706,0.001487931,0.0003849578,0.2121618,0.009098145,0.008084253,0.3378143,0.3727491],"study_design_scores_gemma":[0.0004864042,0.00111506,0.0652243,0.0003198765,0.00006724067,0.0006547869,0.0007369744,0.7773293,0.01690083,0.01156619,0.1254495,0.0001494792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6513622,0.002565068,0.04608402,0.002065411,0.001441911,0.00090733,0.2190824,0.02151122,0.05498043],"genre_scores_gemma":[0.6038385,0.0004460725,0.05670687,0.0005116786,0.00006490317,0.0003635795,0.3128984,0.0005872125,0.02458266],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03022126,"threshold_uncertainty_score":0.06009072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1476525840134473,"score_gpt":0.3518252212266105,"score_spread":0.2041726372131632,"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."}}