{"id":"W2044229894","doi":"10.1016/s0167-9457(02)00125-2","title":"Automatic stride interval extraction from long, highly variable and noisy gait timing signals","year":2002,"lang":"en","type":"article","venue":"Human Movement Science","topic":"Music and Audio Processing","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Holland Bloorview Kids Rehabilitation Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Hospital for Sick Children","keywords":"Probabilistic logic; STRIDE; Computer science; Gait; Noise (video); Pattern recognition (psychology); Time series; Gait analysis; Artificial intelligence; Speech recognition; Statistics; Mathematics; Physical medicine and rehabilitation","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":[0.0006030265,0.0001453303,0.0001484854,0.0001565966,0.0007056936,0.0009135128,0.0008228511,0.00003015159,0.0006384256],"category_scores_gemma":[0.00003826746,0.0001351352,0.00002399245,0.0004869655,0.0002033825,0.00198566,0.0003562768,0.0001164245,0.00004810801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008957944,"about_ca_system_score_gemma":0.00003826055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009983902,"about_ca_topic_score_gemma":0.000006315755,"domain_scores_codex":[0.9981008,0.00002724334,0.0002901647,0.0005995017,0.0006036909,0.0003786281],"domain_scores_gemma":[0.9991809,0.00005913964,0.0001842582,0.0003762086,0.00006364702,0.0001358849],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002110628,0.000368713,0.004538085,0.0001042508,0.00002830641,0.00005322015,0.005395361,0.0006758018,0.6917105,0.02557245,0.005166682,0.2663845],"study_design_scores_gemma":[0.0005259698,0.0001406692,0.02801799,0.0003609745,0.00001741821,0.00000781715,0.0001023203,0.8934848,0.05716928,0.01905658,0.0006206233,0.0004955331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5900632,0.0001453789,0.4058744,0.0003991055,0.0002427932,0.0001161504,0.000001136077,0.0001467095,0.003011221],"genre_scores_gemma":[0.9626007,0.000006012408,0.03500555,0.001622013,0.00007865972,0.000008803797,8.341693e-7,0.000005847553,0.0006715806],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.892809,"threshold_uncertainty_score":0.8809025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04133980181626417,"score_gpt":0.2748854104969481,"score_spread":0.233545608680684,"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."}}