{"id":"W2070312675","doi":"10.1109/embc.2013.6610688","title":"Subspace method decomposition and identification of the parallel-cascade model of ankle joint stiffness: Theory and simulation","year":2013,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research","keywords":"Subspace topology; Cascade; Computer science; Nonlinear system; Monte Carlo method; Stiffness matrix; Noise (video); Stiffness; Algorithm; Replicate; Matrix decomposition; Representation (politics); System identification; Control theory (sociology); Applied mathematics; Mathematics; Data modeling; Artificial intelligence; Engineering; Structural engineering; Statistics","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":[0.002378234,0.00006995435,0.0001588789,0.00007810595,0.00005700449,0.00004769825,0.0001405157,0.00005299841,0.00003122085],"category_scores_gemma":[0.001051638,0.00003898108,0.00003450995,0.0001724719,0.0001019186,0.0002331138,0.00006742749,0.0000450157,0.000002715276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007999363,"about_ca_system_score_gemma":0.00001612993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001827774,"about_ca_topic_score_gemma":0.00000156307,"domain_scores_codex":[0.9987455,0.0002272972,0.0004410846,0.000190084,0.0003244082,0.00007160184],"domain_scores_gemma":[0.9979467,0.001281984,0.0002190415,0.0002898116,0.0002283438,0.00003405705],"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":[0.00001300131,0.00001740358,0.0001528399,0.00001416009,0.000006577036,1.518761e-8,0.0004270104,0.8909712,0.06116505,0.04105834,0.00006611584,0.00610829],"study_design_scores_gemma":[0.00009837827,0.000006921657,0.03172958,0.000008310071,0.00001137972,6.57287e-7,0.0001552397,0.7553982,0.005136115,0.207415,0.000001656486,0.00003857058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3075373,0.0000643957,0.6919401,0.0001409599,0.00002705813,0.0001925997,0.000002397722,0.000008171415,0.00008691753],"genre_scores_gemma":[0.9646348,0.000007984849,0.03488794,0.00001708912,0.000005477303,0.000008031197,5.590328e-7,0.000004166938,0.0004339656],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6570974,"threshold_uncertainty_score":0.1589603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07375303476207182,"score_gpt":0.3547182065713287,"score_spread":0.280965171809257,"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."}}