{"id":"W3183932977","doi":"10.1080/00273171.2021.1941729","title":"Copula-Based Redundancy Analysis","year":2021,"lang":"en","type":"article","venue":"Multivariate Behavioral Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Copula (linguistics); Computer science; Redundancy (engineering); Regression; Regression analysis; Econometrics; Bivariate analysis; Multivariate statistics; Statistics; Data mining; Machine learning; Mathematics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005616077,0.0002211221,0.0004796871,0.0007398117,0.000398125,0.0002143792,0.0004605722,0.0002486002,0.02381769],"category_scores_gemma":[0.0004508025,0.0002176398,0.0003963035,0.006332924,0.0001880094,0.0001023113,0.0002061268,0.0008383268,0.0002266415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003579124,"about_ca_system_score_gemma":0.0004166167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00236804,"about_ca_topic_score_gemma":0.0002124775,"domain_scores_codex":[0.9966883,0.0001626438,0.0003797505,0.0007540543,0.001155906,0.000859309],"domain_scores_gemma":[0.9975749,0.0002898871,0.00007273316,0.0009617504,0.0008069477,0.0002937497],"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":[0.0001281134,0.002146251,0.1582202,0.00007936569,0.0006953385,0.0006754447,0.0001992355,0.0001663815,0.8330694,0.0003755912,0.0009174339,0.003327253],"study_design_scores_gemma":[0.001067183,0.00004999397,0.01811742,0.00001994319,0.001453555,0.000004074864,0.0007399175,0.002255368,0.9720694,0.0001504781,0.003679864,0.0003927543],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9808566,0.000639124,0.001090459,0.0003774743,0.00005250983,0.00007445493,0.0001047743,0.0001608277,0.01664377],"genre_scores_gemma":[0.9815495,0.00001925537,0.002560524,0.00003322075,0.00009503152,0.00005517988,0.0003209971,0.00003623138,0.01533005],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1401028,"threshold_uncertainty_score":0.9770747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2776338551734261,"score_gpt":0.5132476847527523,"score_spread":0.2356138295793262,"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."}}