{"id":"W2016714679","doi":"10.1007/s11634-010-0083-2","title":"Generalized GIPSCAL re-revisited: a fast convergent algorithm with acceleration by the minimal polynomial extrapolation","year":2011,"lang":"en","type":"article","venue":"Advances in Data Analysis and Classification","topic":"Analytical Chemistry and Chromatography","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Korea Institute of Energy Research","keywords":"Extrapolation; Acceleration; Diagonal; Positive definiteness; Algorithm; Representation (politics); Polynomial; Mathematics; Time complexity; Matrix (chemical analysis); Computational complexity theory; Convergence (economics); Applied mathematics; Computer science; Mathematical analysis; Geometry; Positive-definite matrix","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":[0.001288773,0.001261482,0.001683063,0.001093916,0.0007890523,0.001201863,0.002471613,0.001318228,0.008340525],"category_scores_gemma":[0.005473914,0.0004964425,0.0009891825,0.001519283,0.0007540517,0.001281099,0.002461435,0.002640448,0.003989259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005732554,"about_ca_system_score_gemma":0.002282296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004641566,"about_ca_topic_score_gemma":0.007381017,"domain_scores_codex":[0.999244,0.000216244,0.00003870369,0.0001184419,0.0002981919,0.00008448344],"domain_scores_gemma":[0.998717,0.0003982656,0.00005964315,0.0003714567,0.0003724073,0.00008126652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008863381,0.0001925649,0.001104145,0.0003966451,0.0001569467,0.0003292676,0.0002457584,0.1470604,0.01600688,0.04374998,0.02329189,0.7665792],"study_design_scores_gemma":[0.00007612089,0.00007254833,0.0002097312,0.00002140864,0.00002141324,0.0001222748,0.00003204234,0.9752192,0.005518249,0.01214049,0.006542898,0.00002366927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009341317,0.0003242138,0.9851313,0.0002370373,0.0001633371,0.00007324365,0.000104689,0.002547387,0.002077571],"genre_scores_gemma":[0.0688623,0.0001507077,0.9255678,0.0001645738,0.00008862489,0.0001436446,0.0003119409,0.0008580252,0.003852332],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008340525,"threshold_uncertainty_score":0.02790189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03893491462284368,"score_gpt":0.2847833179878061,"score_spread":0.2458484033649624,"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."}}