{"id":"W2036786720","doi":"10.1016/j.jbiomech.2015.04.007","title":"Soft tissue artifact distribution on lower limbs during treadmill gait: Influence of skin markers' location on cluster design","year":2015,"lang":"en","type":"article","venue":"Journal of Biomechanics","topic":"Diabetic Foot Ulcer Assessment and Management","field":"Medicine","cited_by":40,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Clayoquot Biosphere Trust","keywords":"Kinematics; Gait; Artifact (error); Thigh; Motion capture; Computer science; Soft tissue; Treadmill; Displacement (psychology); Cluster (spacecraft); Biomedical engineering; Anatomy; Artificial intelligence; Medicine; Physics; Motion (physics); Physical medicine and rehabilitation; Pathology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005297884,0.0002787428,0.000315757,0.0005547527,0.0002123791,0.0003696425,0.0001899958,0.0003004046,0.001203804],"category_scores_gemma":[0.004037487,0.0001494625,0.0001991915,0.0004048632,0.000193131,0.0002413149,0.0002831022,0.0001611518,0.0002080423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009807724,"about_ca_system_score_gemma":0.0002031661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001006411,"about_ca_topic_score_gemma":0.002038098,"domain_scores_codex":[0.9996614,0.0001159196,0.00003477896,0.00006634926,0.00007685016,0.00004463042],"domain_scores_gemma":[0.9981781,0.0009952986,0.0002170485,0.000121418,0.0003699694,0.0001181575],"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":[0.02787859,0.0008602532,0.2262294,0.0007987554,0.0004342849,0.001998355,0.001743297,0.01238734,0.5555143,0.0002864309,0.001023363,0.1708456],"study_design_scores_gemma":[0.00009776767,0.002614406,0.9262481,0.00006926528,0.0004235933,0.002556991,0.0006029131,0.02250949,0.0438899,0.0001501269,0.0007916566,0.00004573211],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9886754,0.0001652651,0.01064898,0.00001956193,0.00002352313,0.00002708507,0.00009726382,0.00005249002,0.0002903593],"genre_scores_gemma":[0.997797,0.00004798113,0.001810283,0.00001323862,0.00001062645,0.00001339738,0.00007345934,0.00002759003,0.0002064321],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001203804,"threshold_uncertainty_score":0.004027128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02087389092232397,"score_gpt":0.2821733188612834,"score_spread":0.2612994279389594,"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."}}