{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03113682,0.001464194,0.002708383,0.002374345,0.001134672,0.002426028,0.004166379,0.002272093,0.001571465],"category_scores_gemma":[0.1318477,0.0006009472,0.001433504,0.003815003,0.0035345,0.002903403,0.00397903,0.003375536,0.0007520352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001236959,"about_ca_system_score_gemma":0.002882528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001394888,"about_ca_topic_score_gemma":0.001086828,"domain_scores_codex":[0.9761998,0.01356651,0.001202069,0.003267883,0.00509607,0.0006676205],"domain_scores_gemma":[0.9153252,0.05914476,0.005804322,0.01150659,0.007449134,0.0007700165],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001390471,0.0003037168,0.01808746,0.0008728399,0.000845566,0.0009528277,0.001046096,0.181717,0.02092936,0.2949067,0.007591133,0.4713568],"study_design_scores_gemma":[0.0001054871,0.0002613473,0.003666382,0.00006600608,0.0001387713,0.0004263057,0.00008685906,0.8364683,0.0185713,0.1347457,0.005349009,0.0001145295],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003184067,0.0001631111,0.9958153,0.0000817933,0.0000423858,0.00005390449,0.00005418027,0.0003701843,0.0002350496],"genre_scores_gemma":[0.3311767,0.0004474451,0.6640282,0.000473781,0.0002808148,0.0009170629,0.0006059861,0.0003665368,0.001703556],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03113682,"threshold_uncertainty_score":0.1646692,"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."}}