{"id":"W2128728535","doi":"10.1002/wics.101","title":"Principal component analysis","year":2010,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":10417,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"","keywords":"Principal component analysis; Singular value decomposition; Correspondence analysis; Dimensionality reduction; Jackknife resampling; Multiple correspondence analysis; Mathematics; Multivariate statistics; Dimension (graph theory); Sparse PCA; Table (database); Similarity (geometry); Data set; Computer science; Statistics; Pattern recognition (psychology); Data mining; Artificial intelligence; Algorithm; Combinatorics","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":[0.005573798,0.002300716,0.002548561,0.00614561,0.001008432,0.004919659,0.002179685,0.001254599,0.02312955],"category_scores_gemma":[0.01580208,0.0005000395,0.001686012,0.01120016,0.001089519,0.002264177,0.001767802,0.001658011,0.01675403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001360734,"about_ca_system_score_gemma":0.003337514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002369246,"about_ca_topic_score_gemma":0.002135433,"domain_scores_codex":[0.9937184,0.001709673,0.0005553531,0.001074072,0.002759356,0.0001832254],"domain_scores_gemma":[0.9929363,0.001995392,0.0005898437,0.0005512601,0.003760498,0.0001666956],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001108005,0.00007144103,0.001766664,0.003087232,0.0004114819,0.0001294294,0.0003032611,0.003491997,0.002285382,0.01256381,0.05097509,0.9248033],"study_design_scores_gemma":[0.000110824,0.0003346015,0.01984403,0.002763531,0.0007068094,0.00104866,0.001065667,0.02829432,0.007202701,0.06213108,0.8762189,0.0002788907],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.01131403,0.0698798,0.8308361,0.00362756,0.002657899,0.003630007,0.008993336,0.006058489,0.06300286],"genre_scores_gemma":[0.1065522,0.07844515,0.7478409,0.001368617,0.001588114,0.004472014,0.01559888,0.001344912,0.04278931],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.02312955,"threshold_uncertainty_score":0.07737607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1009026244551046,"score_gpt":0.3998046590931104,"score_spread":0.2989020346380057,"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."}}