{"id":"W2504522969","doi":"10.1061/(asce)cp.1943-5487.0000615","title":"Evaluation of Vision-Based Measurements for Shake-Table Testing of Nonstructural Components","year":2016,"lang":"en","type":"article","venue":"Journal of Computing in Civil Engineering","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer vision; Displacement (psychology); Shake; Artificial intelligence; Earthquake shaking table; Computer science; Tracking (education); Match moving; Frame rate; Motion (physics); Engineering; Structural engineering; Mechanical 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002523523,0.000155534,0.000390991,0.0003839303,0.00002043046,0.000007818901,0.0002228905,0.00007190505,0.000003677463],"category_scores_gemma":[0.001086926,0.000126825,0.00007803101,0.0002918095,0.00001727205,0.000142305,0.00002228859,0.0001438376,8.877088e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004101556,"about_ca_system_score_gemma":0.00009467391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000100782,"about_ca_topic_score_gemma":0.00000305912,"domain_scores_codex":[0.9978857,0.00004267564,0.0009703909,0.00009312732,0.0007527048,0.0002554032],"domain_scores_gemma":[0.99784,0.000677318,0.000390617,0.0001349091,0.0008976313,0.00005953261],"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":[0.0000141174,0.0000109517,0.01447983,0.0003781219,0.00003305134,5.302447e-7,0.00005093266,0.7680382,0.1811216,0.000005642138,0.00001324124,0.03585383],"study_design_scores_gemma":[0.001372611,0.0001093119,0.1501047,0.003356405,0.00003843986,0.00001132195,0.000004860201,0.7626069,0.0821402,0.0001200638,0.000008783359,0.0001264259],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9443082,0.0001977223,0.0543553,0.000009229347,0.000804654,0.0002200216,0.000003935296,0.00006163453,0.00003933939],"genre_scores_gemma":[0.9251256,0.000002165101,0.07471652,0.000001122393,0.0001214751,0.000002653319,4.052538e-7,0.00002988085,1.690548e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1356249,"threshold_uncertainty_score":0.5171775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07811794655865176,"score_gpt":0.3229868158263712,"score_spread":0.2448688692677194,"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."}}