{"id":"W2765221530","doi":"10.1007/s41237-017-0042-8","title":"Simultaneous canonical correlation analysis with invariant canonical loadings","year":2017,"lang":"en","type":"article","venue":"Behaviormetrika","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Canonical correlation; Canonical analysis; Canonical correspondence analysis; Mathematics; Canonical form; Covariance; Invariant (physics); Applied mathematics; Statistics; Pure mathematics; Mathematical physics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000456938,0.00018795,0.0004504733,0.00005301147,0.0008128703,0.0003324245,0.0004446027,0.0001603968,0.001345928],"category_scores_gemma":[0.001020601,0.00006848769,0.0002256944,0.0007738877,0.0001914487,0.0001424828,0.00007810205,0.0002297331,0.0000370152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005707509,"about_ca_system_score_gemma":0.00002427826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003914868,"about_ca_topic_score_gemma":0.008354365,"domain_scores_codex":[0.9983026,0.0001367965,0.0003242013,0.0004729659,0.0004085499,0.0003548933],"domain_scores_gemma":[0.9980273,0.001089965,0.0002532169,0.0002425852,0.0001291194,0.0002577996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002481282,0.0004289883,0.6768809,0.000003526663,0.000432517,0.0004088453,0.0000518886,0.0005435717,0.02252182,0.00334074,0.0000969909,0.2950421],"study_design_scores_gemma":[0.0002146183,0.0003784289,0.9889513,0.000007692985,0.00147193,0.00001488022,0.00005172194,0.005986492,0.0005283004,0.00006662445,0.002029246,0.0002987446],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950599,0.00002881066,0.002711205,0.0003560439,0.0000648986,0.000138598,0.00007575135,0.0000448242,0.001519979],"genre_scores_gemma":[0.9957883,0.00001039577,0.003009892,0.00008235274,0.0001022203,0.00001000447,0.00007280758,0.000001511678,0.0009225447],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3120704,"threshold_uncertainty_score":0.999567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03982958838864918,"score_gpt":0.3037183495961985,"score_spread":0.2638887612075493,"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."}}