{"id":"W2934287236","doi":"10.1109/cvprw.2019.00311","title":"GolfDB: A Video Database for Golf Swing Sequencing","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Sports Dynamics and Biomechanics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Nvidia","keywords":"Swing; Computer science; Benchmark (surveying); Event (particle physics); Baseline (sea); Minimum bounding box; Real-time computing; Artificial intelligence; Database; Engineering","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":[0.0003529487,0.001468214,0.0006662211,0.002970078,0.0004244577,0.0005300554,0.001056503,0.001309629,0.01010609],"category_scores_gemma":[0.001832143,0.0002650089,0.0004708201,0.002044314,0.0002205544,0.0008051916,0.0008807883,0.0006331888,0.00652747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005284567,"about_ca_system_score_gemma":0.0007533916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01757573,"about_ca_topic_score_gemma":0.03452801,"domain_scores_codex":[0.9995647,0.00004297073,0.00005333309,0.0001218848,0.0001560721,0.00006097601],"domain_scores_gemma":[0.9994191,0.00007804711,0.00005509408,0.0001145676,0.0002357398,0.00009751675],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002102362,0.0006885343,0.01671753,0.002592221,0.0002522178,0.0009903039,0.0003210763,0.005709686,0.01973967,0.001529512,0.6835764,0.2657804],"study_design_scores_gemma":[0.0008967785,0.001049561,0.2337067,0.0009708854,0.000215735,0.002979896,0.00122048,0.09565036,0.034182,0.005198391,0.6236143,0.0003149138],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.09532327,0.002546039,0.01893562,0.0003751383,0.0005062679,0.0008897989,0.8542507,0.01672392,0.0104493],"genre_scores_gemma":[0.06704935,0.0005629517,0.01656041,0.0001055706,0.00007038571,0.0004228343,0.9118863,0.0004238992,0.002918332],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01757573,"threshold_uncertainty_score":0.03494686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02311966373605329,"score_gpt":0.2322113143529244,"score_spread":0.2090916506168711,"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."}}