{"id":"W4403181753","doi":"10.1007/s10439-024-03622-w","title":"Validation of Markerless Motion Capture for Soldier Movement Patterns Assessment Under Varying Body-Borne Loads","year":2024,"lang":"en","type":"article","venue":"Annals of Biomedical Engineering","topic":"Occupational Health and Performance","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Defence Research and Development Canada; Wilfrid Laurier University; Carleton University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Motion capture; Movement (music); Motion (physics); Body segment; Computer science; Physical medicine and rehabilitation; Computer vision; Artificial intelligence; Physics; Acoustics; Medicine","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":[0.0009937487,0.0005950913,0.000405118,0.000804726,0.0002057485,0.0005284162,0.0006158327,0.001035603,0.001247796],"category_scores_gemma":[0.003031418,0.0002167744,0.0003028654,0.0004187825,0.0002451093,0.0004203081,0.0005814933,0.0002570591,0.0005981415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009928652,"about_ca_system_score_gemma":0.0002631851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001576424,"about_ca_topic_score_gemma":0.002687873,"domain_scores_codex":[0.9993739,0.0001517831,0.0000421726,0.0001671497,0.00020379,0.0000611725],"domain_scores_gemma":[0.9989536,0.0003556581,0.0001128893,0.0001319049,0.0003994785,0.00004635638],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.002668953,0.0007927546,0.1014419,0.0009201738,0.0003685256,0.0003067982,0.0009330765,0.01478225,0.5258223,0.0004481927,0.002058306,0.3494566],"study_design_scores_gemma":[0.0002261719,0.004444874,0.6412504,0.0002001118,0.0005096178,0.001812184,0.0007024615,0.2231079,0.121808,0.0006073277,0.0052,0.0001308915],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.819522,0.0006347427,0.1757967,0.0001170785,0.0001374294,0.0002506183,0.001257472,0.000694608,0.001589366],"genre_scores_gemma":[0.9567788,0.0003046636,0.04010641,0.0001194864,0.00003771568,0.0001970157,0.000982658,0.00004269229,0.001430534],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001576424,"threshold_uncertainty_score":0.005255461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07179414283059031,"score_gpt":0.4448937810222868,"score_spread":0.3730996381916964,"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."}}