{"id":"W3134227279","doi":"10.1109/access.2021.3062748","title":"Development and Validation of a Deep Learning Algorithm and Open-Source Platform for the Automatic Labelling of Motion Capture Markers","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Human Motion and Animation","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Labelling; Data set; Motion capture; Motion (physics); Set (abstract data type); Artificial neural network; Software; Transfer of learning; Pattern recognition (psychology); Algorithm; Computer vision","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":[],"consensus_categories":[],"category_scores_codex":[0.000180727,0.00005065636,0.00008449326,0.00002977283,0.00007040268,0.00007627317,0.0000661795,0.00002969838,0.00001594336],"category_scores_gemma":[0.00002045959,0.00004323728,0.000009761327,0.00006816089,0.00001058091,0.0002343112,0.00002527208,0.00004630977,2.465671e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001344283,"about_ca_system_score_gemma":0.000006982842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000563877,"about_ca_topic_score_gemma":0.00000958162,"domain_scores_codex":[0.9996506,0.000009488157,0.0001550395,0.00006508087,0.00006430209,0.00005546487],"domain_scores_gemma":[0.999772,0.00006691884,0.00005521103,0.00004346246,0.00004774121,0.00001464349],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000477365,0.0000154438,0.0005505545,0.0006426958,0.00007075789,3.996503e-7,0.005232932,0.05434103,0.005411258,0.00009073774,0.0000301484,0.9336092],"study_design_scores_gemma":[0.000354689,0.000006381922,0.005441713,0.00008906497,0.00002114481,0.000003013424,0.0008767741,0.9290826,0.06348271,0.00007245508,0.0005010394,0.00006843419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5491914,0.0001348404,0.4504063,0.00001020918,0.00004163419,0.0001196178,0.00000107412,0.00002004519,0.00007497073],"genre_scores_gemma":[0.9859998,0.00004287685,0.01385869,0.000007719563,0.00001207936,0.00001392782,0.00001429881,0.00000995794,0.00004070675],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9335408,"threshold_uncertainty_score":0.1763165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02864497911645777,"score_gpt":0.266829946648864,"score_spread":0.2381849675324062,"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."}}