{"id":"W3133447707","doi":"10.1177/1045389x20972474","title":"Broadband signal reconstruction for SHM: An experimental and numerical time reversal methodology","year":2021,"lang":"en","type":"article","venue":"Journal of Intelligent Material Systems and Structures","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"Nvidia","keywords":"Structural health monitoring; SIGNAL (programming language); Acoustics; Acoustic emission; Broadband; Lamb waves; Compensation (psychology); Signal processing; Transfer function; Electronic engineering; Engineering; Computer science; Structural engineering; Surface wave; Physics; Telecommunications; Electrical 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":[0.0002341611,0.0001065243,0.0002716634,0.00004503654,0.00005144668,0.0001304795,0.00003725065,0.00008722561,0.00009412067],"category_scores_gemma":[0.00003610505,0.00008575989,0.00003764083,0.00002445048,0.0000274245,0.0001204415,0.00001023219,0.00007124,2.436e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003008778,"about_ca_system_score_gemma":0.00001719227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009573337,"about_ca_topic_score_gemma":2.925967e-7,"domain_scores_codex":[0.9992604,0.00007938003,0.0003625194,0.0001002354,0.00008769173,0.0001097656],"domain_scores_gemma":[0.999597,0.00007331623,0.0001088903,0.00004584813,0.00009353672,0.00008147186],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001106287,0.000007626597,0.00003203055,0.00007895944,0.00006569755,0.000007988148,0.0002397259,0.002145055,0.9895748,0.0005511528,0.0001176331,0.007068651],"study_design_scores_gemma":[0.0004922993,0.0005124933,0.0001734432,0.00009472134,0.00007480972,0.003372505,0.001058745,0.03364281,0.9574627,0.001648725,0.001241409,0.0002253893],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9069068,0.001514694,0.08961441,0.00001135842,0.001804514,0.00009126282,0.00002704027,0.00001182523,0.00001812161],"genre_scores_gemma":[0.9892995,0.00008627482,0.009931032,0.000006879055,0.0006380465,0.000002238492,0.000007619411,0.00001371674,0.00001470384],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08239271,"threshold_uncertainty_score":0.3497187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0240497786740169,"score_gpt":0.2625817682360697,"score_spread":0.2385319895620528,"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."}}