{"id":"W2103521794","doi":"10.1177/0962280215591236","title":"Global tests for novelty","year":2015,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"Jenny ja Antti Wihurin Rahasto; Natural Sciences and Engineering Research Council of Canada; Turun Yliopistosäätiö; Magnus Ehrnroothin Säätiö","keywords":"Novelty; Novelty detection; Computer science; Outlier; Permutation (music); Set (abstract data type); Relation (database); Range (aeronautics); Data mining; Null hypothesis; Resampling; Machine learning; Artificial intelligence; Statistical hypothesis testing; Data set; Statistics; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01279014,0.000079773,0.0001739969,0.0001097033,0.0000935112,0.00007557866,0.001044795,0.0001513062,0.00009239811],"category_scores_gemma":[0.03190062,0.00006721531,0.00002818048,0.001234674,0.0003413478,0.00007954263,0.0004504028,0.0004150536,0.00003081181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002057636,"about_ca_system_score_gemma":0.0006435743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001206346,"about_ca_topic_score_gemma":0.00003875633,"domain_scores_codex":[0.9966846,0.0008686337,0.0003100586,0.0004350201,0.001179484,0.0005222083],"domain_scores_gemma":[0.9926899,0.005756736,0.00002323712,0.0004299892,0.0003855427,0.0007146089],"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":[0.000006534765,0.00005105287,0.00007929924,0.000005713438,0.000001127334,0.000007229956,0.00001342761,4.724032e-7,0.00000986533,0.5099526,0.007327065,0.4825456],"study_design_scores_gemma":[0.000340833,0.0002454374,0.001947125,0.0000144159,8.432899e-7,0.00001354939,0.00002586702,0.1061651,0.0001694246,0.81485,0.07614131,0.00008604013],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00004496147,0.00004913501,0.9864033,0.004389458,0.0001085965,0.0003675337,0.00002262259,0.00009987647,0.008514485],"genre_scores_gemma":[0.01436711,0.00001006161,0.9847503,0.00025579,0.00008236313,0.0003970107,0.000002754268,0.000005675339,0.0001289259],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4824595,"threshold_uncertainty_score":0.9762541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3512734504430494,"score_gpt":0.6502540705804974,"score_spread":0.2989806201374481,"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."}}