{"id":"W2141633607","doi":"10.1016/j.jbiomech.2015.09.025","title":"Kinematic gait patterns in healthy runners: A hierarchical cluster analysis","year":2015,"lang":"en","type":"article","venue":"Journal of Biomechanics","topic":"Lower Extremity Biomechanics and Pathologies","field":"Engineering","cited_by":97,"is_retracted":false,"has_abstract":false,"ca_institutions":"Running Injury Clinic; University of Calgary","funders":"Core Research for Evolutional Science and Technology; Canadian Institutes of Health Research; Alberta Innovates; Alberta Innovates - Health Solutions; University of Calgary","keywords":"Sagittal plane; Kinematics; Gait; Ankle; Physical medicine and rehabilitation; Hierarchical clustering; Gait analysis; Principal component analysis; Cluster (spacecraft); Cluster analysis; Knee Joint; Computer science; Medicine; Mathematics; Artificial intelligence; Anatomy","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":[0.0009332618,0.0003974195,0.0007092881,0.002110099,0.0009488477,0.000780088,0.0004588398,0.0002930108,0.002733255],"category_scores_gemma":[0.002042325,0.0002056867,0.0008144908,0.001225123,0.0004038937,0.000317372,0.0007695928,0.0002435213,0.0005620289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003483255,"about_ca_system_score_gemma":0.0009134702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01335274,"about_ca_topic_score_gemma":0.01886077,"domain_scores_codex":[0.9993512,0.0001214854,0.00007989464,0.0001922647,0.0001182646,0.0001368389],"domain_scores_gemma":[0.9991069,0.0001915818,0.00008320952,0.0001123439,0.0003857754,0.0001201229],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005291232,0.0009584566,0.7905941,0.000284602,0.001067814,0.0006071095,0.003978007,0.006127375,0.03298245,0.0005724815,0.002790402,0.154746],"study_design_scores_gemma":[0.00003927912,0.0005593651,0.9832063,0.00002982825,0.0002049856,0.0002500571,0.001852205,0.0118229,0.001041023,0.000457483,0.0004946203,0.00004196281],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.992604,0.00009209689,0.005891528,0.00003539129,0.00001567343,0.000113122,0.0006780385,0.00007039688,0.0004999727],"genre_scores_gemma":[0.9946035,0.00004103184,0.003703662,0.000007855023,0.000008993459,0.0000728299,0.0008648431,0.0000229653,0.000674405],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01335274,"threshold_uncertainty_score":0.02655005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02844514244506664,"score_gpt":0.2589624744302054,"score_spread":0.2305173319851387,"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."}}