{"id":"W2049642972","doi":"10.1016/j.laa.2011.01.002","title":"A new family of constrained principal component analysis (CPCA)","year":2011,"lang":"en","type":"article","venue":"Linear Algebra and its Applications","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria; McGill University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Singular value decomposition; Principal component analysis; Mathematics; Column (typography); Orthogonal matrix; Singular value; Decomposition; Matrix (chemical analysis); Component (thermodynamics); Ridge; Space (punctuation); Orthogonal transformation; Eigenvalues and eigenvectors; Applied mathematics; Pure mathematics; Algorithm; Orthogonal basis; Statistics; Geometry; Computer science; Chemistry","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00003910298,0.0001152648,0.0002674494,0.0001503566,0.00006733347,0.000008025899,0.0001604655,0.0000771553,0.002231241],"category_scores_gemma":[0.00001396133,0.0001072285,0.0001167314,0.0008586774,0.00006060332,0.00003687825,0.00004470394,0.00009131179,0.00002825685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009900383,"about_ca_system_score_gemma":0.00004263281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001151594,"about_ca_topic_score_gemma":0.000008603663,"domain_scores_codex":[0.999235,0.000004631854,0.0002694025,0.0002325841,0.0001172625,0.0001411102],"domain_scores_gemma":[0.9993584,0.00004709332,0.0001248611,0.0002674942,0.00006638917,0.000135746],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008895936,0.001024375,0.02896184,0.0002467014,0.005001749,0.000003445725,0.001475,0.00002458053,0.743355,0.210797,0.0004579318,0.008563426],"study_design_scores_gemma":[0.001260738,0.00007251694,0.02377605,0.00001434122,0.005834293,0.000009159357,0.00114411,0.005324748,0.9455733,0.002709642,0.01363966,0.0006413935],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8947763,0.001508511,0.04443877,0.0001048641,0.000009031296,0.0001660566,0.000113261,0.00008178783,0.05880142],"genre_scores_gemma":[0.9916903,0.0001269701,0.006527255,0.00003446149,0.00005797912,0.00003041127,0.00005505019,0.000008838718,0.001468746],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2080874,"threshold_uncertainty_score":0.9986808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03129435247461975,"score_gpt":0.2717535594939084,"score_spread":0.2404592070192887,"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."}}