{"id":"W2086821499","doi":"10.1016/j.jbiomech.2014.04.017","title":"Classification accuracy of a single tri-axial accelerometer for training background and experience level in runners","year":2014,"lang":"en","type":"article","venue":"Journal of Biomechanics","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"University of Calgary","keywords":"Accelerometer; Linear discriminant analysis; Computer science; Mathematics; Physical medicine and rehabilitation; Pattern recognition (psychology); Artificial intelligence; 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":[0.0005536033,0.0005678259,0.0004711395,0.0006353084,0.0002264374,0.0007075341,0.0002614642,0.0008669395,0.002025532],"category_scores_gemma":[0.001681267,0.0001797044,0.0004629664,0.0002939852,0.0001670771,0.0002985958,0.000350971,0.0002435977,0.001002333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001240794,"about_ca_system_score_gemma":0.0001628213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004141025,"about_ca_topic_score_gemma":0.007242161,"domain_scores_codex":[0.9996829,0.00004179142,0.00003496745,0.0001081954,0.00005034771,0.00008196092],"domain_scores_gemma":[0.9990883,0.0002984331,0.00015541,0.00005321786,0.0002552738,0.0001494542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001576524,0.000212401,0.9552773,0.00005737763,0.000189534,0.00008287979,0.0001944518,0.001207365,0.01161377,0.00002086066,0.000387333,0.02918012],"study_design_scores_gemma":[0.000009374796,0.000303096,0.9930211,0.00001280972,0.00007817477,0.00009531391,0.0002506952,0.004929396,0.001162213,0.00002782212,0.00009944414,0.00001058733],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998426,0.0001205336,0.0005942293,0.0000298695,0.00002338437,0.000004825717,0.0003742978,0.00002512735,0.0004019178],"genre_scores_gemma":[0.9988764,0.0000494839,0.0002081388,0.00001100781,0.00001084023,0.000004945458,0.0003389702,0.000004301317,0.0004958282],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004141025,"threshold_uncertainty_score":0.008233845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1918036421861229,"score_gpt":0.3094308442863534,"score_spread":0.1176272021002305,"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."}}