{"id":"W4393049901","doi":"10.1111/sms.14605","title":"Differences in running technique between runners with better and poorer running economy and lower and higher milage: An artificial neural network approach","year":2024,"lang":"en","type":"article","venue":"Scandinavian Journal of Medicine and Science in Sports","topic":"Lower Extremity Biomechanics and Pathologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Eurostars","keywords":"Running economy; Sagittal plane; Kinematics; Ankle; Swing; Physical medicine and rehabilitation; Knee flexion; Artificial neural network; Mathematics; Coronal plane; Trunk; Gait cycle; Computer science; Physical therapy; Medicine; Artificial intelligence; Engineering; Physics; Anatomy; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00113807,0.0001471883,0.0003167539,0.0004104078,0.00007070194,0.0001174756,0.00008980782,0.00006399048,0.00001379076],"category_scores_gemma":[0.000006983199,0.00009760985,0.000009832517,0.0003948076,0.0005377202,0.0004551072,0.00003402445,0.0003276828,2.025105e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002053068,"about_ca_system_score_gemma":0.00002312784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001931729,"about_ca_topic_score_gemma":0.000006332975,"domain_scores_codex":[0.9990228,0.00000956574,0.0003087284,0.0002409484,0.0001577242,0.0002602321],"domain_scores_gemma":[0.9996552,0.00003237057,0.00005550012,0.00006588383,0.00001703785,0.000173992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001737935,0.000005932756,0.9787452,0.0000742234,0.00000526568,0.0005137792,0.001900335,0.00004430051,0.0004732817,0.0001381039,0.00005971792,0.01802252],"study_design_scores_gemma":[0.0004294708,0.0005365915,0.9821693,0.001760339,0.0000339335,0.0007588606,0.001661595,0.006338557,0.00005468055,0.005806869,0.0001522644,0.0002975977],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968004,0.001983693,0.0002354878,0.0004044008,0.00019962,0.00008918713,0.000001432502,0.00001517239,0.0002705915],"genre_scores_gemma":[0.9979444,0.0001404951,0.001615132,0.00006179522,0.0002202462,0.000003123783,0.000001239853,0.000009357935,0.000004284368],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01772492,"threshold_uncertainty_score":0.3980414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02514089456721372,"score_gpt":0.2461311266706664,"score_spread":0.2209902321034527,"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."}}