{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02507155,0.003152653,0.00150575,0.005061144,0.002899548,0.004988351,0.001911276,0.002065493,0.02171592],"category_scores_gemma":[0.1398755,0.001238679,0.002151436,0.003834612,0.001607307,0.00313176,0.001885642,0.004856546,0.01003106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009763248,"about_ca_system_score_gemma":0.004504221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003478361,"about_ca_topic_score_gemma":0.008516105,"domain_scores_codex":[0.9886445,0.005594587,0.001590147,0.001305679,0.002613829,0.0002513617],"domain_scores_gemma":[0.947477,0.03188904,0.002834667,0.004293211,0.01304215,0.0004639496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000382362,0.0004338027,0.004355975,0.001594308,0.0003054258,0.000411111,0.003095307,0.009757415,0.01906575,0.03101104,0.05773108,0.8718564],"study_design_scores_gemma":[0.001068483,0.003397759,0.0246268,0.003105354,0.000945964,0.002166839,0.005377405,0.3396228,0.1025104,0.1400428,0.3754826,0.001652932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004548513,0.0001979636,0.9841749,0.0004554398,0.0002902618,0.00163769,0.0005325642,0.004938117,0.003224617],"genre_scores_gemma":[0.01317953,0.0001370378,0.9835597,0.00009379034,0.00005797741,0.001093743,0.000233475,0.0005911231,0.001053539],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02507155,"threshold_uncertainty_score":0.1325926,"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."}}