{"id":"W2528416847","doi":"10.1139/cjce-2015-0417","title":"A new collection of compressed damage indices for multi-damage detection of cold formed steel shear walls based on neural network ensembles","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Principal component analysis; Artificial neural network; Structural engineering; Computer science; Pattern recognition (psychology); Modal; Series (stratigraphy); Artificial intelligence; Engineering; Materials science; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006678165,0.0008949818,0.0007432399,0.001234558,0.0002698934,0.0005097332,0.0006362021,0.0004431525,0.0005817253],"category_scores_gemma":[0.001745905,0.0002968396,0.0005184514,0.0008676448,0.00019445,0.001129086,0.0006041016,0.0007298827,0.000166853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003089684,"about_ca_system_score_gemma":0.0002954711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00168719,"about_ca_topic_score_gemma":0.002897295,"domain_scores_codex":[0.9995764,0.00006589805,0.00003239113,0.0001028699,0.000192653,0.0000297523],"domain_scores_gemma":[0.9993833,0.0001515756,0.00009719888,0.00007762403,0.0002545777,0.00003568767],"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":[0.0001204796,0.0001871737,0.010677,0.000116924,0.0002466143,0.0001294698,0.0001091037,0.5345115,0.03349584,0.002186498,0.001089825,0.4171296],"study_design_scores_gemma":[0.000002100547,0.00003896334,0.002734368,0.000006751276,0.00002600146,0.00003163639,0.000008582922,0.9927405,0.003572781,0.0004787968,0.0003493756,0.00001024495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0902198,0.0003169136,0.9073035,0.00006067586,0.00004615659,0.00005154412,0.0001676846,0.0003812475,0.001452518],"genre_scores_gemma":[0.7399933,0.0003120987,0.2573472,0.00004589764,0.0000782479,0.0001340588,0.0006088344,0.00005142523,0.001428947],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00168719,"threshold_uncertainty_score":0.003531754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01918394074836453,"score_gpt":0.2368384825309266,"score_spread":0.217654541782562,"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."}}