{"id":"W2998868794","doi":"10.48550/arxiv.2001.04433","title":"Towards Automated Swimming Analytics Using Deep Neural Networks","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Analytics; Data collection; Computer science; Tracking (education); Work (physics); Data analysis; Data science; Scale (ratio); Artificial intelligence; Data mining; Engineering; Cartography; Geography","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"],"consensus_categories":[],"category_scores_codex":[0.0001017155,0.0002737004,0.0002905085,0.0001806621,0.0002474395,0.0001891426,0.001512082,0.0002861689,0.00001091672],"category_scores_gemma":[0.00001200322,0.000335383,0.000229163,0.001127726,0.00006763374,0.0002448887,0.002000972,0.0006013566,0.00001245459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002152516,"about_ca_system_score_gemma":0.00009063631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001760835,"about_ca_topic_score_gemma":0.00001170654,"domain_scores_codex":[0.9983997,0.00006846378,0.000224347,0.0009310116,0.00007227116,0.0003041973],"domain_scores_gemma":[0.9984601,0.00002889725,0.0002779833,0.000912275,0.0001339141,0.0001868834],"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":[0.000003963241,0.0000244965,0.0002314078,0.00001768242,0.00004442437,0.0000880341,0.00004836652,0.9620528,0.00003052676,0.03567246,0.00006055235,0.00172535],"study_design_scores_gemma":[0.00009327817,0.00002376823,0.0003026695,0.0000171739,0.00006633199,0.000007838044,0.00002028767,0.9921061,0.0001017557,0.006794697,0.0001317213,0.0003343927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02206996,0.00003073925,0.9744421,0.000152633,0.0002340732,0.0002324737,0.000004168817,0.002288731,0.0005450734],"genre_scores_gemma":[0.9773105,0.00003899898,0.02227765,0.000173375,0.000100978,0.0000011024,0.000009940432,0.00001979764,0.00006761665],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9552406,"threshold_uncertainty_score":0.9999098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0952943346761203,"score_gpt":0.2202412368902031,"score_spread":0.1249469022140828,"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."}}