{"id":"W2890276122","doi":"10.1371/journal.pone.0203839","title":"Using wearable sensors to classify subject-specific running biomechanical gait patterns based on changes in environmental weather conditions","year":2018,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Lower Extremity Biomechanics and Pathologies","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"Running Injury Clinic; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Cadence; Wearable computer; Gait; Random forest; Gait analysis; Computer science; Artificial intelligence; Biomechanics; Machine learning; Simulation; Physical medicine and rehabilitation; 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":[0.0003396687,0.0006799005,0.0004624967,0.0005406894,0.0001322585,0.0004048339,0.0002658186,0.0004881402,0.0004986476],"category_scores_gemma":[0.0008267389,0.0001571621,0.0004788296,0.000466918,0.0001202833,0.0004045154,0.0002277256,0.0002580167,0.0003002583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001159125,"about_ca_system_score_gemma":0.0001526203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002446391,"about_ca_topic_score_gemma":0.003415232,"domain_scores_codex":[0.9997916,0.00003131328,0.00001803374,0.00008890717,0.0000460038,0.00002425035],"domain_scores_gemma":[0.9998378,0.0000504655,0.00003566282,0.00001777734,0.00004962174,0.000008717158],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006571649,0.0008356544,0.2343583,0.0003919982,0.00052001,0.0002934526,0.0002883467,0.1377092,0.1235663,0.0003803448,0.001603945,0.4993952],"study_design_scores_gemma":[0.00002032113,0.0005509073,0.1841878,0.00006244877,0.0001547231,0.0002960266,0.000157384,0.7959795,0.01721638,0.0005550796,0.000781779,0.00003770371],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7867289,0.000562561,0.2099621,0.00008659845,0.000107681,0.0001077645,0.0006897895,0.0005971094,0.001157467],"genre_scores_gemma":[0.9642944,0.0002998909,0.03385923,0.0000376007,0.00002655086,0.00007934307,0.0006229223,0.0000134004,0.0007666355],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002446391,"threshold_uncertainty_score":0.004864335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06977070000985686,"score_gpt":0.2382633521341518,"score_spread":0.1684926521242949,"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."}}