{"id":"W2108832093","doi":"10.1016/j.csda.2004.11.011","title":"Linear grouping using orthogonal regression","year":2004,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":77,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; McMaster University; York University","funders":"Mitacs","keywords":"Hyperplane; Principal component analysis; Linear regression; Cluster analysis; Resampling; Mathematics; Computer science; Linear model; Data set; Set (abstract data type); Algorithm; Artificial intelligence; Statistics; Combinatorics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004431318,0.001416942,0.002252375,0.002960188,0.001512561,0.00229263,0.001693278,0.000818344,0.01214209],"category_scores_gemma":[0.01120484,0.0009672905,0.002659951,0.004445523,0.001447476,0.002908697,0.004074329,0.002046696,0.007271374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006397591,"about_ca_system_score_gemma":0.001948268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001451839,"about_ca_topic_score_gemma":0.001988021,"domain_scores_codex":[0.9953337,0.001810874,0.0002733952,0.001231454,0.0009130964,0.0004374087],"domain_scores_gemma":[0.9950546,0.00112306,0.0003992307,0.001914165,0.001288039,0.0002208164],"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":[0.0006709774,0.0003180564,0.002155262,0.0002906819,0.0002456297,0.0001147514,0.0006884835,0.03271936,0.02354347,0.116457,0.01175349,0.8110428],"study_design_scores_gemma":[0.0001039182,0.0005379712,0.003257558,0.00008336088,0.0002591354,0.0002667225,0.0004044197,0.729622,0.03519893,0.1850871,0.04499514,0.0001837761],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004302584,0.0000504915,0.9930566,0.00004267361,0.00008315802,0.00005990767,0.00008697715,0.001081263,0.001236333],"genre_scores_gemma":[0.05673004,0.0001244225,0.9346566,0.00006029981,0.00007631033,0.0003055993,0.0006084957,0.0009596243,0.006478687],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01214209,"threshold_uncertainty_score":0.04061937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3269742727101041,"score_gpt":0.5135506723646595,"score_spread":0.1865763996545554,"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."}}