{"id":"W4206172589","doi":"10.1007/s00521-021-06518-1","title":"Brain age prediction using improved twin SVR","year":2022,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"Science and Engineering Research Board; Council of Scientific and Industrial Research, India","keywords":"Support vector machine; Structural risk minimization; Minification; Singularity; Computation; Computer science; Regression; Mathematics; Matrix (chemical analysis); Mathematical optimization; Algorithm; Artificial intelligence; 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":[0.0009287936,0.0004498349,0.000928837,0.0008219191,0.0002508668,0.000568435,0.0007971176,0.0008930924,0.001608058],"category_scores_gemma":[0.003292422,0.0003325846,0.0007497536,0.0008006625,0.0001975143,0.0009700911,0.0008545312,0.001036697,0.0007529466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002648858,"about_ca_system_score_gemma":0.0005425696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004797798,"about_ca_topic_score_gemma":0.004523821,"domain_scores_codex":[0.9997509,0.00006274719,0.00001302471,0.00008827582,0.00004533739,0.00003974667],"domain_scores_gemma":[0.9990453,0.0002918918,0.00006967478,0.0001329619,0.0004030047,0.00005712302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001252264,0.0003121964,0.04683639,0.000108643,0.0004598944,0.000410852,0.00009481447,0.4975304,0.0109616,0.006622205,0.008647352,0.4267634],"study_design_scores_gemma":[0.000005049314,0.00001723666,0.001109442,0.000002798398,0.00001923848,0.00004789039,0.000005823331,0.9964857,0.0008081006,0.001218471,0.0002736127,0.000006608107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1905296,0.000786268,0.8044026,0.0002913957,0.00027952,0.00003083694,0.0005679099,0.001359807,0.001752098],"genre_scores_gemma":[0.9261056,0.0001804522,0.06872386,0.00007099227,0.00005638082,0.00003390583,0.000601162,0.0001082467,0.004119413],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004797798,"threshold_uncertainty_score":0.009539723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01661326527126657,"score_gpt":0.2754262135943404,"score_spread":0.2588129483230738,"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."}}