{"id":"W3103290077","doi":"","title":"Learning Agent Representations for Ice Hockey","year":2020,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; Simon Fraser University","funders":"","keywords":"Ice hockey; Computer science; Artificial intelligence; Physical medicine and rehabilitation","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0002262369,0.0001345129,0.0001539945,0.0000911451,0.0004207859,0.001301416,0.0005138456,0.00005634648,0.00000206016],"category_scores_gemma":[0.0003780658,0.0001288528,0.00005612269,0.000446806,0.0000191889,0.004490741,0.0001088194,0.0001876175,0.000101576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004448575,"about_ca_system_score_gemma":0.00007558681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001071258,"about_ca_topic_score_gemma":9.941041e-8,"domain_scores_codex":[0.9985535,0.00005219582,0.0005785152,0.0001793473,0.000386395,0.0002500444],"domain_scores_gemma":[0.9987999,0.00008247342,0.0004829256,0.0001843993,0.0003286811,0.0001216642],"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":[0.000004873108,0.000002391796,0.00018468,0.0003344762,0.000006364539,4.304865e-7,0.006519821,0.977268,0.0000903442,0.002246302,0.002408316,0.010934],"study_design_scores_gemma":[0.0002933573,0.0001183908,0.00007301492,0.00003661356,0.000005711699,0.000008609643,0.0008192462,0.9522479,0.000162139,0.000008578631,0.04608553,0.0001409256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0009000495,0.00004949937,0.9928561,0.002067955,0.0004601809,0.0004973409,0.000001313928,0.0005671746,0.002600405],"genre_scores_gemma":[0.9894168,0.000003507742,0.008622762,0.001206153,0.0001602564,0.00009783705,0.00004608873,0.00001025444,0.0004363178],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9885167,"threshold_uncertainty_score":0.9997354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04542303430578692,"score_gpt":0.2864625706532663,"score_spread":0.2410395363474794,"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."}}