{"id":"W4309152186","doi":"10.1061/9780784484449.064","title":"Real-Life Investigations of Inverse Filtering for Frequency Identification of Bridges Using Smartphones in Passing Vehicles","year":2022,"lang":"en","type":"article","venue":"Lifelines 2022","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Bridge (graph theory); Accelerometer; Filter (signal processing); Computer science; Acceleration; Vibration; Global Positioning System; Identification (biology); Suspension (topology); Real-time computing; Acoustics; Computer vision; Telecommunications","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":[0.000311927,0.0001051559,0.0002097019,0.0002709268,0.0001037819,0.000008163659,0.0001702772,0.00003942352,0.000007420846],"category_scores_gemma":[0.000185385,0.000130114,0.00004869807,0.000421274,0.00004304011,0.000132977,0.00007001976,0.0001268379,1.449619e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000170889,"about_ca_system_score_gemma":0.00007500989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009901932,"about_ca_topic_score_gemma":0.00004708954,"domain_scores_codex":[0.9987824,0.00005176242,0.0006834207,0.000144448,0.000174826,0.0001631343],"domain_scores_gemma":[0.9993681,0.0001211142,0.0001852271,0.0002139648,0.00006773146,0.00004387924],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001523209,0.00001788513,0.04513678,0.0008755321,0.00001664417,0.000001153527,0.001051481,0.1104624,0.8394257,0.00037463,0.000327998,0.002294548],"study_design_scores_gemma":[0.0004498544,0.00007073871,0.1820768,0.000198489,0.00003459977,0.000006991081,0.001343149,0.5339617,0.27732,0.003989241,0.0002046808,0.0003437471],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966006,0.0001947658,0.001784594,0.00007338484,0.0007597862,0.00029457,0.0001316318,0.0001544895,0.000006165384],"genre_scores_gemma":[0.9798157,0.00007078279,0.01966229,0.00001172872,0.0002251075,0.0001370942,0.00003933628,0.0000317808,0.000006153652],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5621057,"threshold_uncertainty_score":0.5305893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05376479650961895,"score_gpt":0.3126213691113538,"score_spread":0.2588565726017348,"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."}}