{"id":"W3156920115","doi":"10.1111/biom.13479","title":"Feature screening with large‐scale and high‐dimensional survival data","year":2021,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Actua; Western University","funders":"National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; National Science Foundation","keywords":"Covariate; Computer science; Sample size determination; Dimension (graph theory); Big data; Variable (mathematics); Scale (ratio); Data mining; Feature (linguistics); Sample (material); Computation; Variables; Machine learning; Statistics; Mathematics; Algorithm","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008222791,0.0007943322,0.001396079,0.001912199,0.0009935441,0.001267232,0.00172165,0.001576135,0.002084521],"category_scores_gemma":[0.04525118,0.0004455803,0.001491652,0.001789396,0.001354191,0.001909185,0.002324927,0.001716042,0.0005359059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005743558,"about_ca_system_score_gemma":0.001553191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002242706,"about_ca_topic_score_gemma":0.002141564,"domain_scores_codex":[0.996192,0.002220951,0.0002175694,0.0005001395,0.0007008818,0.0001684107],"domain_scores_gemma":[0.9657028,0.02710252,0.001888716,0.003335107,0.001442352,0.0005285937],"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":[0.0009342163,0.0006147699,0.07050002,0.001069895,0.0005167478,0.002888025,0.001186494,0.2151069,0.01437554,0.1475923,0.01747932,0.5277359],"study_design_scores_gemma":[0.00007941244,0.0001744451,0.01018632,0.0000852458,0.00006373658,0.0006962256,0.0001703318,0.8220392,0.003660168,0.1588577,0.003921462,0.00006574299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0228529,0.0002136233,0.9749982,0.0004708833,0.00005346881,0.0001524967,0.0001953515,0.0005553864,0.0005075491],"genre_scores_gemma":[0.5789601,0.0004660505,0.4162638,0.000555115,0.0002395042,0.0006572339,0.0009146885,0.0001234857,0.001819993],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008222791,"threshold_uncertainty_score":0.04348677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1567047904430262,"score_gpt":0.380795179429857,"score_spread":0.2240903889868309,"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."}}