{"id":"W2995973437","doi":"10.48550/arxiv.1912.05107","title":"PuckNet: Estimating hockey puck location from broadcast video","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Offensive; Visibility; Event (particle physics); Artificial intelligence; Computer vision; Geography; Engineering","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001571613,0.0002953267,0.000288062,0.0002583294,0.0001823145,0.0002949792,0.001590825,0.0002944807,0.0001522735],"category_scores_gemma":[0.00005796572,0.0003649073,0.0001530544,0.0004163973,0.000047186,0.0007986837,0.001502394,0.0005542409,0.001859751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002182638,"about_ca_system_score_gemma":0.0002004931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000528071,"about_ca_topic_score_gemma":0.00003067682,"domain_scores_codex":[0.9980839,0.0001230964,0.000238613,0.001145317,0.0001225845,0.0002865255],"domain_scores_gemma":[0.9976289,0.0001232437,0.0003766346,0.001499137,0.0002428897,0.0001291776],"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":[0.00003546255,0.0002198017,0.002563773,0.0002161871,0.0002309828,0.0001912361,0.001056188,0.9262816,0.0002085187,0.02177913,0.004281901,0.04293523],"study_design_scores_gemma":[0.0003947954,0.00002927365,0.0013821,0.00024042,0.00005583192,0.000003014853,0.00005354741,0.9480228,0.0002932309,0.04799536,0.001083613,0.0004460375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1970608,0.00005791067,0.7979827,0.00009970504,0.001461254,0.0002606323,0.00002063164,0.0003049602,0.00275145],"genre_scores_gemma":[0.9874935,0.0000533842,0.01049829,0.0001842018,0.0002889655,0.000001400286,0.0001351594,0.00001995131,0.001325103],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7904328,"threshold_uncertainty_score":0.9998803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06757904189271983,"score_gpt":0.1886950354282621,"score_spread":0.1211159935355422,"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."}}