{"id":"W2588902562","doi":"10.1002/stc.1998","title":"Damage detection under varying temperature using artificial neural networks","year":2017,"lang":"en","type":"article","venue":"Structural Control and Health Monitoring","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":111,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Novelty detection; Artificial neural network; Robustness (evolution); Computer science; Artificial intelligence; Pattern recognition (psychology); Structural health monitoring; Noise (video); Biological system; Novelty; Engineering; Structural engineering","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":[0.0006383768,0.0005128628,0.0003977425,0.0006971114,0.0001912578,0.0003589744,0.0005384204,0.0006482886,0.0002999279],"category_scores_gemma":[0.001526123,0.0002371242,0.0004115781,0.000391857,0.0003406616,0.0007463738,0.0004636249,0.0003506762,0.00008822717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003992386,"about_ca_system_score_gemma":0.0001369261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00100091,"about_ca_topic_score_gemma":0.001472981,"domain_scores_codex":[0.9996318,0.00007072729,0.00002641107,0.00009938483,0.0001394183,0.00003225436],"domain_scores_gemma":[0.9992938,0.0002754789,0.000199044,0.00005678446,0.0001531249,0.00002178656],"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":[0.0005420576,0.0001679412,0.01255295,0.0001982757,0.0001716396,0.0003839582,0.0001765365,0.5409634,0.1336975,0.001398017,0.0004199678,0.3093278],"study_design_scores_gemma":[0.000003347675,0.00004774471,0.002828618,0.000004835211,0.00001088905,0.00004757913,0.00001055387,0.9876214,0.008873376,0.0004390403,0.0001012482,0.00001139912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3279373,0.0004037271,0.6698993,0.00008362232,0.00004466884,0.00003473631,0.00006156116,0.0005081626,0.001026961],"genre_scores_gemma":[0.9398179,0.000112845,0.05939545,0.00002269594,0.00001626613,0.00002624465,0.00005112508,0.00001106573,0.0005464115],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00100091,"threshold_uncertainty_score":0.003376126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04608285620975124,"score_gpt":0.3392630013724993,"score_spread":0.2931801451627481,"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."}}