{"id":"W2895083861","doi":"10.3390/s18103329","title":"Gait Type Analysis Using Dynamic Bayesian Networks","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Gait analysis; Gait; Bayesian probability; Dynamic Bayesian network; Computer science; Type (biology); Bayesian network; Physical medicine and rehabilitation; Artificial intelligence; Medicine; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005391337,0.0001039162,0.0001693933,0.0002455967,0.00006171418,0.00003107372,0.00005855186,0.00006454271,0.0008140867],"category_scores_gemma":[0.000008586091,0.0001046294,0.0001304348,0.001288817,0.00003339757,0.0000326402,0.000008937117,0.00008066164,0.0001554379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000374206,"about_ca_system_score_gemma":0.000003705448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002477762,"about_ca_topic_score_gemma":0.0002022868,"domain_scores_codex":[0.9994346,0.00001901397,0.0001366161,0.0001292648,0.00008090192,0.0001995731],"domain_scores_gemma":[0.9996695,0.00001263685,0.00001946577,0.0001638642,0.00006316071,0.0000713784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003169325,0.000007803384,0.001800862,0.000006280875,0.0008711589,0.000007406803,0.0001094393,0.9922533,0.0007307864,0.000009536763,0.0001757678,0.004024458],"study_design_scores_gemma":[0.00005183052,0.000007072574,0.001716385,0.000005264397,0.0004664712,0.000002094084,0.00006511705,0.9969169,0.0001188383,0.00002234153,0.0004972035,0.0001305061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9530839,0.00006860785,0.04100741,0.0000125364,0.0002154989,0.00002908844,0.000003294052,0.0002222556,0.00535736],"genre_scores_gemma":[0.9980821,0.00003103983,0.001297191,0.00004349224,0.0001290943,3.979094e-7,0.00002228541,0.00002114895,0.0003732557],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04499815,"threshold_uncertainty_score":0.8913679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009373543899570104,"score_gpt":0.2354328714805125,"score_spread":0.2260593275809424,"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."}}