{"id":"W2168189648","doi":"10.1109/ccece.2005.1556946","title":"Identification of time-varying joint dynamics","year":2006,"lang":"en","type":"article","venue":"","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Identification (biology); Basis (linear algebra); Monte Carlo method; System identification; Joint (building); Noise (video); Artificial intelligence; Data mining; Mathematics; Engineering","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":[0.0004202712,0.0004823758,0.0004828589,0.000382455,0.0002783914,0.000378993,0.0004555306,0.0005188789,0.001129213],"category_scores_gemma":[0.001874175,0.0002510082,0.000404031,0.0003320555,0.000350194,0.0007503025,0.0005470216,0.0007385964,0.0003312657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002365403,"about_ca_system_score_gemma":0.0005973103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001872931,"about_ca_topic_score_gemma":0.001627142,"domain_scores_codex":[0.9997076,0.00005389305,0.00001159365,0.00008243894,0.0001204366,0.00002396343],"domain_scores_gemma":[0.9995559,0.0001897705,0.00009057609,0.00008316281,0.00006676166,0.00001387889],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001100441,0.0000601291,0.002606585,0.0001129372,0.00008330992,0.0001801024,0.0001875097,0.7282003,0.07402483,0.02200496,0.0005286153,0.1719007],"study_design_scores_gemma":[0.000003012548,0.00002336942,0.0009359436,0.000005027614,0.000006248803,0.00006103104,0.00001182718,0.9894676,0.005622223,0.003154194,0.000699671,0.000009905867],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02140924,0.0000653331,0.9773563,0.00003477567,0.00001428652,0.00001603755,0.00003195656,0.0002944632,0.0007776306],"genre_scores_gemma":[0.7621577,0.0003263925,0.2344683,0.00004177059,0.00002095875,0.000102145,0.0001752821,0.00007755253,0.002629876],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001872931,"threshold_uncertainty_score":0.003777564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01041863525116861,"score_gpt":0.2505123605357852,"score_spread":0.2400937252846166,"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."}}