{"id":"W3204743385","doi":"10.1007/978-3-030-76004-5_12","title":"An Unsupervised Deep Auto-encoder with One-Class Support Vector Machine for Damage Detection","year":2021,"lang":"en","type":"book-chapter","venue":"Conference proceedings of the Society for Experimental Mechanics","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Support vector machine; Autoencoder; Class (philosophy); Encoder; Artificial intelligence; Computer science; Pattern recognition (psychology); Deep learning; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001282604,0.0005018894,0.0005172816,0.0000362955,0.0002196103,0.0001078964,0.0004914482,0.0004426698,0.00006853315],"category_scores_gemma":[0.00001085444,0.0004211119,0.0006897775,0.00005111784,0.00006548918,0.0002623569,0.0001011633,0.0003895977,6.687401e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003098318,"about_ca_system_score_gemma":0.00008130123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002895275,"about_ca_topic_score_gemma":0.000005767029,"domain_scores_codex":[0.9984943,0.000001313621,0.0003554668,0.0004432331,0.000311677,0.0003939819],"domain_scores_gemma":[0.9989154,0.00001912304,0.0002134071,0.0002316771,0.0005235593,0.00009685381],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001124366,0.00004094704,0.000001875124,0.0009916162,0.00062478,2.724682e-7,0.003174967,0.00009634842,0.9109728,0.08223747,0.0002196136,0.001526888],"study_design_scores_gemma":[0.00102049,0.0007362998,0.000001797077,0.0004647079,0.0003190376,0.000009820151,0.003034765,0.1253919,0.8597435,0.005568444,0.003041853,0.000667415],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1324194,0.005600907,0.7231168,0.0005361706,0.01920087,0.02705563,0.003258053,0.003840055,0.08497206],"genre_scores_gemma":[0.97883,0.00008340069,0.01544868,0.00007358244,0.0004037569,0.0003751041,0.00009596587,0.0002585705,0.004430976],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8464105,"threshold_uncertainty_score":0.999824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01420877598932424,"score_gpt":0.2205567801161436,"score_spread":0.2063480041268194,"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."}}