{"id":"W2900517774","doi":"10.5539/ijsp.v8n1p40","title":"A Sub-Model Theorem for Ordinary Least Squares","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ordinary least squares; Mathematics; Selection (genetic algorithm); Variable (mathematics); Least-squares function approximation; Feature selection; Mathematical optimization; Model selection; Applied mathematics; Generalized least squares; Statistics; Computer science; Artificial intelligence; Mathematical analysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002748187,0.00008980382,0.0001661318,0.00006868796,0.00006024425,0.00005656851,0.0002152284,0.00004643872,0.0002883168],"category_scores_gemma":[0.0005407506,0.00007255052,0.0000637585,0.0000500037,0.0002047686,0.00008386482,0.00003858902,0.0001052563,0.000001303911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006862667,"about_ca_system_score_gemma":0.00009050145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007209612,"about_ca_topic_score_gemma":0.000009012208,"domain_scores_codex":[0.99918,0.00000827244,0.0003385347,0.0001228556,0.00024177,0.0001085843],"domain_scores_gemma":[0.9982716,0.000251854,0.0002481383,0.0000810702,0.00107833,0.00006898881],"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":[0.007237935,0.002205522,0.06617407,0.0008573549,0.003097787,0.00008532829,0.002046004,0.0004730989,0.1201797,0.58838,0.04116952,0.1680937],"study_design_scores_gemma":[0.001387619,0.0004293267,0.001696887,0.00005522471,0.0002322131,0.0001427054,0.0001465309,0.01828724,0.06066562,0.9132212,0.003498525,0.00023692],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5565318,0.0003238583,0.4388937,0.0005913586,0.0002209887,0.00005682299,0.0008162749,0.00001072574,0.002554468],"genre_scores_gemma":[0.9491777,0.00005865392,0.05013015,0.00007037818,0.000388965,0.000002826148,0.00001283049,0.000007638988,0.0001508723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3926459,"threshold_uncertainty_score":0.3156867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02347082903721092,"score_gpt":0.3194291431579394,"score_spread":0.2959583141207285,"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."}}