{"id":"W4392659383","doi":"10.3390/a17030114","title":"Deep-Shallow Metaclassifier with Synthetic Minority Oversampling for Anomaly Detection in a Time Series","year":2024,"lang":"en","type":"article","venue":"Algorithms","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Enbridge; Natural Sciences and Engineering Research Council of Canada","keywords":"Anomaly detection; Computer science; Oversampling; Anomaly (physics); Artificial intelligence; Deep learning; Pipeline (software); Series (stratigraphy); Machine learning; Time series; Pattern recognition (psychology); Geology","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":[0.002420529,0.0006934303,0.0008076883,0.0005547654,0.0004071244,0.0006539567,0.001482237,0.0009087602,0.001015174],"category_scores_gemma":[0.004512881,0.0002169237,0.0005386118,0.0003955818,0.0005217589,0.001121167,0.001092121,0.001574815,0.0003247896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000809764,"about_ca_system_score_gemma":0.001074431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005548791,"about_ca_topic_score_gemma":0.006590328,"domain_scores_codex":[0.9993773,0.0001555627,0.00004221745,0.0001743458,0.0001703316,0.00008030672],"domain_scores_gemma":[0.9986192,0.0005761521,0.0001361037,0.0002575756,0.0003172077,0.00009370249],"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":[0.00112265,0.000658545,0.01588004,0.0001020021,0.0001782694,0.0002583739,0.0002723113,0.3906372,0.03361839,0.008965435,0.004880988,0.5434257],"study_design_scores_gemma":[0.000007730729,0.0000411769,0.0003439653,0.000002288496,0.000005681969,0.00002742245,0.000007756628,0.9934907,0.004784794,0.0009423512,0.0003415886,0.000004572044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.206271,0.0004424202,0.7865584,0.0004623367,0.0001180911,0.0000994703,0.0001597363,0.00451123,0.001377266],"genre_scores_gemma":[0.7906228,0.00006807513,0.2067771,0.0002735655,0.00004298229,0.00006573811,0.0004143546,0.0001089925,0.00162626],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005548791,"threshold_uncertainty_score":0.01280111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01238834257019814,"score_gpt":0.2431211221181453,"score_spread":0.2307327795479472,"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."}}