{"id":"W1838682225","doi":"10.1002/cem.2700","title":"Simple methods for the optimization of complex‐valued kurtosis as a projection index","year":2015,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; University of Prince Edward Island","funders":"Innovation PEI; National Aeronautics and Space Administration","keywords":"Projection pursuit; Kurtosis; Projection (relational algebra); Computer science; Maxima and minima; Algorithm; Principal component analysis; Simple (philosophy); Transformation (genetics); Multivariate statistics; Orthographic projection; Data mining; Index (typography); Mathematical optimization; Artificial intelligence; Mathematics; Machine learning; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001501273,0.0001531459,0.000472304,0.0009353579,0.00008014277,0.00005037238,0.0003973096,0.0001541115,0.0003235928],"category_scores_gemma":[0.006698926,0.0001071381,0.0002939572,0.004055763,0.00006897328,0.0001747271,0.00004989528,0.0002543831,9.434952e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002228586,"about_ca_system_score_gemma":0.0002359519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003334824,"about_ca_topic_score_gemma":5.35238e-7,"domain_scores_codex":[0.9983761,0.000039979,0.0007241385,0.0001320774,0.0005256304,0.0002020701],"domain_scores_gemma":[0.9961593,0.000898684,0.001202102,0.0002355976,0.001376351,0.0001279416],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00620602,0.004880599,0.06442147,0.002254502,0.009055616,0.00001824982,0.004354815,0.2255673,0.3794257,0.001536895,0.09617277,0.2061061],"study_design_scores_gemma":[0.00344968,0.0005549119,0.0003451229,0.00002595131,0.001436838,0.0001353095,0.003414785,0.1427476,0.8260404,0.002127985,0.01941278,0.0003086371],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01924789,0.001948085,0.9741998,0.0003621329,0.0001679675,0.000127709,0.000009396002,0.00001735084,0.00391971],"genre_scores_gemma":[0.8123459,0.000280903,0.1858897,0.000134377,0.0004702793,0.00001180149,0.00001219674,0.0000388558,0.0008160406],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.793098,"threshold_uncertainty_score":0.8019724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1073682465068941,"score_gpt":0.4229925563932687,"score_spread":0.3156243098863746,"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."}}