{"id":"W1998713996","doi":"10.1080/10255842.2012.673594","title":"A simple approach to guide factor retention decisions when applying principal component analysis to biomechanical data","year":2012,"lang":"en","type":"article","venue":"Computer Methods in Biomechanics & Biomedical Engineering","topic":"Musculoskeletal pain and rehabilitation","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Principal component analysis; Data set; Monte Carlo method; Multivariate statistics; Set (abstract data type); Dimensionality reduction; Computer science; Dimension (graph theory); Range (aeronautics); Divergence (linguistics); Function (biology); Statistics; Factor analysis; Component (thermodynamics); Statistical power; Data mining; Mathematics; Artificial intelligence; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004732834,0.0004101207,0.0009339732,0.002489936,0.00007786499,0.00005343073,0.0007460255,0.0003540776,0.00006182917],"category_scores_gemma":[0.001518472,0.0003552509,0.0003080337,0.0040535,0.00003394889,0.000184078,0.001430787,0.000437109,0.00003615629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004094983,"about_ca_system_score_gemma":0.00005702209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005132764,"about_ca_topic_score_gemma":0.00000154732,"domain_scores_codex":[0.9957297,0.0003143889,0.001155929,0.001014156,0.0008655526,0.0009202626],"domain_scores_gemma":[0.9961047,0.0007143453,0.0001000462,0.001523209,0.0001024503,0.001455307],"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":[0.0000672873,0.001205134,0.0001920801,0.0004131121,0.0006963432,0.000006398472,0.0009180118,0.0006184494,0.7158753,0.001299564,0.0008346394,0.2778737],"study_design_scores_gemma":[0.0006925708,0.0002626531,0.005738569,0.0001610297,0.0002382177,0.00001180387,0.00007505274,0.935941,0.001147273,0.0001090842,0.05515595,0.000466843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03314285,0.0001251981,0.9638487,0.0002954076,0.0009023763,0.001381692,0.00006286416,0.0002178261,0.00002308123],"genre_scores_gemma":[0.1643342,0.000009455144,0.8338705,0.0003616724,0.0004876148,0.00014334,0.0007249572,0.00005180427,0.0000164681],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9353225,"threshold_uncertainty_score":0.99989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09008472120708454,"score_gpt":0.3957955412929409,"score_spread":0.3057108200858564,"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."}}