{"id":"W3097464213","doi":"10.1016/j.jmva.2020.104693","title":"Dynamic tilted current correlation for high dimensional variable screening","year":2020,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"Fundamental Research Funds for the Central Universities","keywords":"Mathematics; Spurious relationship; Variable (mathematics); Independence (probability theory); Curse of dimensionality; Consistency (knowledge bases); Correlation; Current (fluid); Distance correlation; Ordinary least squares; Feature selection; Projection (relational algebra); Statistics; Algorithm; Random variable; Computer science; Machine learning","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":[],"consensus_categories":[],"category_scores_codex":[0.0002270238,0.00009769035,0.0002149398,0.0001223387,0.00006869326,0.00002092042,0.0001278581,0.00007539886,0.0000427302],"category_scores_gemma":[0.0001888068,0.00008212105,0.0002345819,0.0003866309,0.00001255914,0.000009163525,0.00003271444,0.00009919002,0.000001403539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001634577,"about_ca_system_score_gemma":0.00007159264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006246095,"about_ca_topic_score_gemma":0.000001767388,"domain_scores_codex":[0.9990757,0.00006332037,0.0003835387,0.0001850853,0.0001839856,0.0001083655],"domain_scores_gemma":[0.99892,0.00002245151,0.000471329,0.0001175628,0.0003580126,0.0001106434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006263262,0.00009032214,0.001651243,0.00001504982,0.00116753,6.993839e-7,0.00006653288,0.1847071,0.7964321,0.0001324006,0.002031239,0.01307939],"study_design_scores_gemma":[0.003319079,0.0004847561,0.03265369,0.00004418187,0.002345255,0.000004494251,0.00007178308,0.9029096,0.02416998,0.0001695743,0.03350681,0.00032076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0912443,0.0004825905,0.90737,0.0006117627,0.0001771653,0.0000778164,0.00001741762,0.000003874111,0.00001507222],"genre_scores_gemma":[0.9692825,0.00005454782,0.02998183,0.0001790633,0.0001896795,0.000004711054,0.0002046509,0.00001065185,0.00009235903],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8780382,"threshold_uncertainty_score":0.3348799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01923887636457,"score_gpt":0.2946339540878971,"score_spread":0.2753950777233271,"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."}}