{"id":"W4293680188","doi":"10.3390/s22166193","title":"Data Augmentation for Deep-Learning-Based Multiclass Structural Damage Detection Using Limited Information","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Government of Ontario","keywords":"Structural health monitoring; Computer science; Identification (biology); Deep learning; Convolutional neural network; Field (mathematics); Scarcity; Machine learning; Artificial intelligence; Risk analysis (engineering); Data science; Engineering; Business","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":[0.0008431033,0.0009711332,0.0007498375,0.0006443844,0.0002356363,0.0005144113,0.001426302,0.000875418,0.001695906],"category_scores_gemma":[0.002586856,0.000437739,0.0007188209,0.0005973855,0.0006375946,0.0009750187,0.001199747,0.001655946,0.0005574748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000708708,"about_ca_system_score_gemma":0.0006975018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003923357,"about_ca_topic_score_gemma":0.005581245,"domain_scores_codex":[0.999671,0.00007174422,0.00002318485,0.0001052309,0.00007477953,0.00005394908],"domain_scores_gemma":[0.9992478,0.0003709746,0.00008648659,0.0001152008,0.0001385528,0.0000409569],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002406239,0.0002430796,0.003230985,0.0001959224,0.00009731921,0.0001360994,0.0001094787,0.6988482,0.007862871,0.002756578,0.00509005,0.2811887],"study_design_scores_gemma":[0.000002331929,0.00002068403,0.0002470776,0.000007248302,0.000004337737,0.0000116597,0.000005600231,0.9970881,0.001083415,0.001223722,0.0003027873,0.000003135453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09854812,0.00217238,0.8912282,0.0006517143,0.0001569277,0.00008291788,0.0007650002,0.003517932,0.002876875],"genre_scores_gemma":[0.8893864,0.0006681867,0.1025673,0.0004712959,0.00008634527,0.0001810778,0.002522283,0.0001331623,0.003983986],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003923357,"threshold_uncertainty_score":0.007801056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01824623420957965,"score_gpt":0.250722101997159,"score_spread":0.2324758677875793,"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."}}