{"id":"W3161470663","doi":"10.1109/cvprw53098.2021.00512","title":"DeepDarts: Modeling Keypoints as Objects for Automatic Scorekeeping in Darts using a Single Camera","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","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; Compute Canada; Nvidia","keywords":"Computer science; Artificial intelligence; Pipeline (software); Generalization; Convolutional neural network; Task (project management); Face (sociological concept); Computer vision; Enhanced Data Rates for GSM Evolution; Image (mathematics); Calibration; Pattern recognition (psychology); Transfer of learning; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007606064,0.0003804055,0.001069424,0.0006360842,0.0001324582,0.0004661722,0.0003017113,0.0003286578,0.0005454765],"category_scores_gemma":[0.0001791887,0.0004852862,0.0003261974,0.0002697681,0.00003148338,0.0002759983,0.0003699997,0.0004181494,0.00003722161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004459754,"about_ca_system_score_gemma":0.0002242451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003864267,"about_ca_topic_score_gemma":0.0009866261,"domain_scores_codex":[0.9969312,0.000009505141,0.001441383,0.000967499,0.00007294339,0.0005774699],"domain_scores_gemma":[0.9984612,0.00004711764,0.0006053959,0.0006730656,0.00009937619,0.0001138593],"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":[0.00001659638,0.0003118158,0.01329882,0.001184542,0.000199102,0.00005095436,0.003346888,0.9696499,0.00003765858,0.009500991,0.00005394631,0.00234877],"study_design_scores_gemma":[0.0004100143,0.00003037232,0.0002211265,0.0006878382,0.00002038119,0.000009954037,0.0004712357,0.9813197,0.00006488373,0.01599277,0.0001821899,0.0005895244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9038048,0.002896864,0.08443362,0.0001164891,0.001100976,0.000630905,0.00005404391,0.00006126517,0.006901016],"genre_scores_gemma":[0.9812806,0.0002373146,0.01733758,0.0004626755,0.000192982,0.00006682408,0.0001093995,0.00007891923,0.0002337183],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07747576,"threshold_uncertainty_score":0.9997599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1133852912115175,"score_gpt":0.2739388309834365,"score_spread":0.160553539771919,"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."}}