{"id":"W1574204554","doi":"10.1002/cjs.11181","title":"On central matrix based methods in dimension reduction","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sliced inverse regression; Dimension (graph theory); Inverse; Variance (accounting); Sufficient dimension reduction; Matrix (chemical analysis); Computer science; Dimensionality reduction; Variance reduction; Mathematics; Regression; Reduction (mathematics); Statistics; Constant (computer programming); Econometrics; Mathematical optimization; Algorithm; Artificial intelligence; Monte Carlo method","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000650881,0.0001175546,0.0002760541,0.000283818,0.00005547898,0.00005233516,0.0001194977,0.00007131342,0.001338166],"category_scores_gemma":[0.005174571,0.0001005486,0.00003685654,0.0001628518,0.0000774002,0.00006125207,0.000003967953,0.0003201333,0.00001684503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002365461,"about_ca_system_score_gemma":0.0006078331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00263343,"about_ca_topic_score_gemma":0.0009830559,"domain_scores_codex":[0.9985335,0.0003123458,0.000550802,0.0001013452,0.0001648518,0.0003370797],"domain_scores_gemma":[0.997172,0.001637964,0.0002417915,0.0001321191,0.0002690856,0.0005470337],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002445009,0.00005464778,0.000549691,0.00005999499,0.00001804147,0.0001567544,0.0003582934,0.0001958654,0.0007952883,0.8817223,0.04188916,0.07417556],"study_design_scores_gemma":[0.0004886097,0.0003306674,0.01223401,0.0001841458,0.00003197348,0.00004793441,0.0001199217,0.009029718,0.0006292412,0.9763135,0.0004210935,0.0001691441],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02177855,0.00002814288,0.9764832,0.0003285291,0.0006737704,0.0001383456,0.000105116,0.000003891813,0.000460459],"genre_scores_gemma":[0.1351316,0.000003566423,0.8646231,0.00008802266,0.00005976751,0.000001933455,0.000002447771,0.0000156663,0.00007387049],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.113353,"threshold_uncertainty_score":0.9995747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07847587495821476,"score_gpt":0.3838524638124837,"score_spread":0.305376588854269,"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."}}