{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002954544,0.00008984315,0.0000831389,0.00009797664,0.00008319614,0.0001315356,0.002680842,0.00003658718,0.000123245],"category_scores_gemma":[0.0001852393,0.0000745554,0.00002509537,0.0006049473,0.00005083619,0.0004847435,0.00142788,0.00009195441,0.01013585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001885246,"about_ca_system_score_gemma":0.00002677061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005602764,"about_ca_topic_score_gemma":0.00007055145,"domain_scores_codex":[0.9987931,0.00002512585,0.0001491426,0.0004711772,0.0002813933,0.0002800528],"domain_scores_gemma":[0.9981205,0.0001696324,0.00003096634,0.001574681,0.00003022524,0.00007401431],"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":[0.000006272644,0.00006725603,0.0009886166,0.000005868895,0.00002994754,0.00009818323,0.002442777,0.002015412,0.0005207137,0.3532873,0.09167594,0.5488617],"study_design_scores_gemma":[0.00002675891,0.00005705228,0.001038374,0.000007712394,0.000002040003,0.000002314544,0.0003077847,0.9434412,0.01201891,0.0146794,0.02823759,0.000180916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01190022,0.00002564061,0.9247845,0.007097509,0.001349025,0.0001972959,0.00000580415,0.001647684,0.05299233],"genre_scores_gemma":[0.9534817,0.00004649002,0.03677772,0.002415724,0.0002183697,0.00001512073,0.00002953906,0.00001857794,0.006996763],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9415815,"threshold_uncertainty_score":0.9906349,"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."}}