{"id":"W4214540111","doi":"10.1080/14763141.2022.2044507","title":"Automatic detection of passing and shooting in water polo using machine learning: a feasibility study","year":2022,"lang":"en","type":"article","venue":"Sports Biomechanics","topic":"Sports Performance and Training","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre de réadaptation Lethbridge-Layton-Mackay; Centre for Interdisciplinary Research in Rehabilitation; Université de Sherbrooke; McGill University","funders":"","keywords":"Inertial measurement unit; Water polo; Artificial intelligence; Throwing; Computer science; Overhead (engineering); Support vector machine; Sensitivity (control systems); Computer vision; Engineering; Physical medicine and rehabilitation; Medicine; Aeronautics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001249866,0.0004703498,0.0004252361,0.0009545389,0.0001979464,0.0004322133,0.0004783666,0.000569909,0.000812956],"category_scores_gemma":[0.003650761,0.0002354737,0.0002801477,0.0005708473,0.0003312966,0.0006499066,0.0003197775,0.0002281916,0.0002981449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003193393,"about_ca_system_score_gemma":0.0004014539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003530081,"about_ca_topic_score_gemma":0.004708404,"domain_scores_codex":[0.9986796,0.0004789516,0.00008387041,0.0002581645,0.0003940158,0.0001054061],"domain_scores_gemma":[0.9972246,0.001461372,0.000332172,0.00011708,0.0007500411,0.0001148385],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002224071,0.001161948,0.3566298,0.0005459327,0.0001728527,0.0006915718,0.0008010417,0.02612216,0.1405722,0.0003075095,0.000479536,0.4702913],"study_design_scores_gemma":[0.0001372886,0.005629597,0.3726155,0.0001036677,0.0001604372,0.001057201,0.001318302,0.5702879,0.0469087,0.0003596611,0.001337852,0.00008388142],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9478309,0.0001437286,0.05080552,0.00006028946,0.00001720266,0.0001562688,0.00008925555,0.0001649146,0.0007318971],"genre_scores_gemma":[0.965816,0.00008325845,0.03349688,0.00002019489,0.00001094763,0.00005752867,0.0001176111,0.000009134349,0.0003884547],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003530081,"threshold_uncertainty_score":0.007019103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0326660595316967,"score_gpt":0.2872893925893867,"score_spread":0.25462333305769,"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."}}