{"id":"W3194896055","doi":"10.1002/cjs.11649","title":"Cellwise outlier detection with false discovery rate control","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Tianjin City; National Natural Science Foundation of China","keywords":"False discovery rate; Outlier; Computer science; Anomaly detection; Data mining; Covariance; Pooling; Exploit; Identification (biology); Multiple comparisons problem; Series (stratigraphy); Statistics; Algorithm; Mathematics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003705503,0.0001425641,0.0003487367,0.00009347231,0.0001230533,0.0001018258,0.0000899551,0.00005414856,0.00008855381],"category_scores_gemma":[0.002213955,0.000114998,0.00004724203,0.0001210965,0.0001068452,0.0001773815,0.000004910795,0.0002825935,0.000003483142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001495831,"about_ca_system_score_gemma":0.001095431,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001652526,"about_ca_topic_score_gemma":0.02062985,"domain_scores_codex":[0.9987955,0.0001756787,0.0004238908,0.0001306647,0.0001708831,0.0003033833],"domain_scores_gemma":[0.997287,0.001072437,0.000280838,0.0001638626,0.0006317521,0.000564147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004257007,0.0001508726,0.0007689355,0.0003594216,0.0005670027,0.0157194,0.001229238,0.002253826,0.005247803,0.7951035,0.007533574,0.1706406],"study_design_scores_gemma":[0.002254565,0.0004625586,0.0007307305,0.0001703505,0.0004110741,0.0006419611,0.0007321163,0.002467838,0.002953054,0.9809075,0.007878209,0.0003900641],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007034068,0.0001437524,0.9912226,0.0001037165,0.0003207987,0.00007862916,0.0008205207,0.00000430226,0.0002716634],"genre_scores_gemma":[0.5087418,0.00001877922,0.4901812,0.0001957728,0.000107089,0.000001892701,0.000003358229,0.00002989081,0.0007201681],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5017077,"threshold_uncertainty_score":0.9972411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04383236623461512,"score_gpt":0.3127935642780972,"score_spread":0.2689611980434821,"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."}}