{"id":"W2771930108","doi":"","title":"Comments on \"Envelope models for parsimonious and efficient multivariate linear regression\" by Cook, D. and Li, B. and Chiaromonte","year":2010,"lang":"en","type":"article","venue":"HKBU Institutional Repository (Hong Kong Baptist University)","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Multivariate statistics; Bayesian multivariate linear regression; Envelope (radar); Linear regression; Regression; Statistics; Econometrics; Mathematics; Computer science; Telecommunications","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":[],"consensus_categories":[],"category_scores_codex":[0.0001757373,0.0002183297,0.000261128,0.00009653925,0.0009315862,0.00004336486,0.0001033274,0.0001409386,0.000001842578],"category_scores_gemma":[0.0002322684,0.000200806,0.00003735848,0.00007142365,0.0004819679,0.0001715582,0.0001298859,0.0002723887,4.756087e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006149121,"about_ca_system_score_gemma":0.00005901077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004074012,"about_ca_topic_score_gemma":0.0000111684,"domain_scores_codex":[0.9988621,0.00006646354,0.0002000184,0.0004503221,0.0001982305,0.0002228707],"domain_scores_gemma":[0.9986897,0.0006625672,0.0001236035,0.0001830273,0.0001089798,0.0002321988],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004635129,0.0003447724,0.0004725772,0.0001395942,0.00008671042,0.0001683809,0.0002674859,0.001609361,0.007438714,0.9844276,0.0003976562,0.004183582],"study_design_scores_gemma":[0.01072587,0.0008181817,0.004289903,0.001105661,0.0004707896,0.0004338935,0.0004264101,0.7969909,0.005537971,0.1334367,0.04388149,0.001882192],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3486125,0.00006193335,0.6471449,0.0001228293,0.0003039127,0.0004323963,0.0001830669,0.00005701564,0.003081507],"genre_scores_gemma":[0.7868328,0.00003829387,0.211273,0.00005733389,0.00005374755,0.000004634209,0.00001606196,0.0000182224,0.001705883],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.850991,"threshold_uncertainty_score":0.8188632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05137397017856358,"score_gpt":0.3241172245211092,"score_spread":0.2727432543425456,"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."}}