{"id":"W2043103176","doi":"10.1007/s11200-013-0254-7","title":"A Newton algorithm for weighted total least-squares solution to a specific errors-in-variables model with correlated measurements","year":2014,"lang":"en","type":"article","venue":"Studia Geophysica et Geodaetica","topic":"Statistical and numerical algorithms","field":"Mathematics","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Covariance matrix; Algorithm; Mathematics; Matrix (chemical analysis); Least-squares function approximation; Mathematical optimization; Applied mathematics; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001706275,0.0011501,0.001138452,0.0005770331,0.0005789652,0.0007218011,0.001628484,0.001598428,0.002530392],"category_scores_gemma":[0.004457461,0.0008420833,0.0009807149,0.001035252,0.0007761365,0.001152345,0.001318864,0.001674494,0.0007598888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000653099,"about_ca_system_score_gemma":0.002172208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01079239,"about_ca_topic_score_gemma":0.01084733,"domain_scores_codex":[0.9994974,0.0001979814,0.00003132787,0.0001010089,0.000137729,0.00003447529],"domain_scores_gemma":[0.9988294,0.0007450694,0.00009262943,0.00006852883,0.0002270544,0.00003724288],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007370986,0.00004079451,0.000512918,0.0001547497,0.0001048542,0.0001041426,0.0001269831,0.8408817,0.00233453,0.03618579,0.002409706,0.1170702],"study_design_scores_gemma":[0.000006524574,0.0000130918,0.00007780605,0.000006181557,0.000008315354,0.00001345783,0.000005519656,0.9936194,0.0002747256,0.004949037,0.001017824,0.000008105288],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001184099,0.00005480345,0.9982799,0.00003341906,0.00002495004,0.00001627986,0.00001582508,0.00007538709,0.0003152554],"genre_scores_gemma":[0.04434463,0.0002198836,0.9513073,0.00006439347,0.00007217312,0.0002587746,0.0002016373,0.0001475625,0.003383582],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01079239,"threshold_uncertainty_score":0.0214591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04487219889102892,"score_gpt":0.2950597009726857,"score_spread":0.2501875020816569,"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."}}