{"id":"W2056625603","doi":"10.1115/1.4026366","title":"Maximizing Sensitivity Vector Fields: A Parametric Study","year":2014,"lang":"en","type":"article","venue":"Journal of Computational and Nonlinear Dynamics","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Civil, Mechanical and Manufacturing Innovation; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Attractor; Sensitivity (control systems); Parametric statistics; Dynamical systems theory; Context (archaeology); Nonlinear system; Set (abstract data type); Dynamical system (definition); Biological system; Computer science; Statistical physics; Mathematics; Physics; Mathematical analysis; Statistics; Biology; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.00676192,0.0009477328,0.0007634021,0.001851898,0.0004491007,0.001031275,0.0006130397,0.001137662,0.001329185],"category_scores_gemma":[0.03094327,0.0003672969,0.000873071,0.00106813,0.001323499,0.001711377,0.001354791,0.00115144,0.00007800035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007148965,"about_ca_system_score_gemma":0.0005575949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001941228,"about_ca_topic_score_gemma":0.000796793,"domain_scores_codex":[0.9985789,0.0008608907,0.00005190092,0.0001463963,0.0002641474,0.00009773938],"domain_scores_gemma":[0.9754928,0.02223082,0.0007429889,0.000642153,0.0007163621,0.0001749797],"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":[0.00009808716,0.00006766429,0.002287799,0.000100428,0.00005399532,0.0001702041,0.0002271314,0.9237155,0.004897831,0.04150017,0.0004044558,0.02647669],"study_design_scores_gemma":[0.000003092634,0.00006216201,0.0005940672,0.00001765022,0.000009877569,0.00007315686,0.00003219176,0.9891018,0.001092543,0.008695864,0.0003022517,0.00001524704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2714595,0.001250737,0.7156789,0.0009664787,0.0000351482,0.0001678676,0.0001243947,0.0002177461,0.01009928],"genre_scores_gemma":[0.9561525,0.0004566374,0.04202617,0.00005230366,0.00003307293,0.00007779262,0.00005553928,0.00005515839,0.001090758],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00676192,"threshold_uncertainty_score":0.03576088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01160654211160527,"score_gpt":0.2676107136118261,"score_spread":0.2560041715002208,"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."}}