{"id":"W3105676231","doi":"10.1117/12.2576447","title":"Anomaly detection using 1D convolutional neural networks for surface enhanced raman scattering","year":2020,"lang":"en","type":"article","venue":"","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Overfitting; Anomaly detection; Artificial intelligence; Pattern recognition (psychology); Outlier; Convolutional neural network; Computer science; Class (philosophy); Identification (biology); Anomaly (physics); Artificial neural network; One-class classification; Machine learning; Support vector machine; Physics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003824291,0.0005166547,0.0003109124,0.0004092104,0.0001739038,0.0004084286,0.0007201082,0.0005146948,0.0004888501],"category_scores_gemma":[0.0006956284,0.0002514274,0.0004763628,0.0003975154,0.000299463,0.0006061823,0.0005025472,0.0006471709,0.0001950531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007245299,"about_ca_system_score_gemma":0.0004207657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006739976,"about_ca_topic_score_gemma":0.008813066,"domain_scores_codex":[0.9998301,0.00002375076,0.000007156868,0.00004957458,0.00006116893,0.00002823963],"domain_scores_gemma":[0.9997994,0.00006072746,0.00003078769,0.00002410624,0.00007252851,0.00001245226],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001498676,0.0001625962,0.006820814,0.0001187444,0.0001324826,0.0002137136,0.00008932332,0.5792277,0.09656517,0.01079501,0.002217904,0.3035066],"study_design_scores_gemma":[6.16723e-7,0.000009094453,0.0002665299,0.000001575984,0.000003293177,0.00001420407,0.000001691542,0.995474,0.003417806,0.0005642245,0.0002429047,0.000003947413],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09234321,0.0006754926,0.9033468,0.0003068808,0.00008978362,0.00002978444,0.000153664,0.001072235,0.001982143],"genre_scores_gemma":[0.7927904,0.0006367959,0.2015512,0.0001553298,0.00004974169,0.00004873946,0.0003642676,0.00005627238,0.004347296],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006739976,"threshold_uncertainty_score":0.01340151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02461229260578808,"score_gpt":0.3188630608268405,"score_spread":0.2942507682210524,"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."}}