{"id":"W4321063693","doi":"10.1109/m2vip55626.2022.10041050","title":"Study on the Reliability of SemGCN in Gait Analysis","year":2022,"lang":"en","type":"article","venue":"","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Intraclass correlation; Artificial intelligence; Computer vision; Computer science; Smoothing; Reliability (semiconductor); Position (finance); Filter (signal processing); Mathematics; Statistics; Reproducibility","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008491942,0.001049849,0.0009591879,0.001578693,0.0005272537,0.000925046,0.0008948517,0.0009446488,0.001058398],"category_scores_gemma":[0.04355735,0.0003494392,0.0008264706,0.001288049,0.0007922722,0.00133027,0.0008287877,0.0006504185,0.0005256094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005086464,"about_ca_system_score_gemma":0.0007227672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006927587,"about_ca_topic_score_gemma":0.005398396,"domain_scores_codex":[0.9943873,0.001778031,0.0004227711,0.001502947,0.001645453,0.0002635493],"domain_scores_gemma":[0.9769617,0.0112729,0.001483194,0.002277689,0.007619502,0.0003851388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002361189,0.0004457654,0.5271146,0.000662027,0.001213314,0.0008059558,0.002360626,0.1072861,0.0158444,0.002027494,0.002785671,0.3370929],"study_design_scores_gemma":[0.00005928387,0.001462675,0.2370338,0.0001783108,0.0004841505,0.001145559,0.001257283,0.7405032,0.01296876,0.002530232,0.002257092,0.0001197091],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8557066,0.001323942,0.1380968,0.000243016,0.0002145359,0.0001187976,0.0003493572,0.0005687378,0.003378224],"genre_scores_gemma":[0.9889749,0.0001983154,0.009669889,0.00003150456,0.00003804338,0.0000396464,0.0004226764,0.00005882224,0.0005661216],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008491942,"threshold_uncertainty_score":0.04491025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01680161721431639,"score_gpt":0.2300212879260207,"score_spread":0.2132196707117043,"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."}}