{"id":"W2602468573","doi":"","title":"Subspace based damage detection technique: investigation on the effect of number of samples","year":2015,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Subspace topology; Computer science; Noise (video); Structural health monitoring; Sensitivity (control systems); Modal; Experimental data; Algorithm; Data mining; Statistics; Artificial intelligence; Mathematics; Structural engineering; Engineering; Materials science; Electronic 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":[0.001168199,0.0006053526,0.0007985701,0.000749073,0.0003367709,0.0004722452,0.0004477171,0.0008221141,0.001231954],"category_scores_gemma":[0.009399873,0.0002283202,0.0003729021,0.0008166848,0.0003785648,0.001182963,0.0005446245,0.0005400638,0.0002650733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001602335,"about_ca_system_score_gemma":0.0003375135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006888427,"about_ca_topic_score_gemma":0.00125236,"domain_scores_codex":[0.9986147,0.0003616337,0.00008955927,0.0002595233,0.0005836631,0.0000910191],"domain_scores_gemma":[0.9890811,0.007057253,0.0006810715,0.001087332,0.001878103,0.0002150155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002512369,0.000635981,0.008973454,0.0005937234,0.0001533486,0.0002791747,0.0003636912,0.1265474,0.3547891,0.001333913,0.001050905,0.502767],"study_design_scores_gemma":[0.00002562719,0.001008358,0.01604796,0.00004471495,0.000121155,0.0007438597,0.0001487418,0.7995867,0.1802259,0.0008321917,0.001165862,0.00004899208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5135676,0.0018239,0.481319,0.0001826446,0.00009194951,0.00007615223,0.0002014427,0.0008813829,0.001855924],"genre_scores_gemma":[0.8556044,0.0007551699,0.1420201,0.00004434898,0.00003443305,0.00003889534,0.0002725532,0.0001137272,0.001116355],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001231954,"threshold_uncertainty_score":0.006178141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0259657667136585,"score_gpt":0.2649786601985061,"score_spread":0.2390128934848476,"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."}}