{"id":"W4387353257","doi":"10.1061/jsendh.steng-12313","title":"Data-Driven Approach for Generating Tricomponent Nonstationary Non-Gaussian Thunderstorm Wind Records Using Continuous Wavelet Transforms and S-Transform","year":2023,"lang":"en","type":"article","venue":"Journal of Structural Engineering","topic":"Seismic Waves and Analysis","field":"Earth and Planetary Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Thunderstorm; Gaussian; Algorithm; Wavelet; Wavelet transform; Computer science; Interval (graph theory); Mathematics; Meteorology; Physics; Artificial intelligence","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":[0.0008253531,0.0004999712,0.0003817052,0.0009994383,0.0002709696,0.0004746171,0.0008784558,0.0005149036,0.0009828364],"category_scores_gemma":[0.002196403,0.0003827052,0.0007367697,0.001102823,0.000258172,0.0006490855,0.0006093552,0.0006792655,0.0005254599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002066304,"about_ca_system_score_gemma":0.0008279072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001807796,"about_ca_topic_score_gemma":0.002799563,"domain_scores_codex":[0.9996518,0.00005837058,0.00002820749,0.00009281309,0.0001468489,0.00002195298],"domain_scores_gemma":[0.9991663,0.0003045113,0.00008487498,0.0001073675,0.0003066377,0.00003032615],"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":[0.0002798709,0.0001632324,0.004518363,0.0002846775,0.0001454703,0.0002723829,0.0002452749,0.2393959,0.07714413,0.01037398,0.001293043,0.6658837],"study_design_scores_gemma":[0.00001289949,0.00004627333,0.001254059,0.000005215684,0.00001946584,0.00006501118,0.00002123155,0.9827207,0.01323546,0.001508749,0.00109444,0.00001641761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01215763,0.00002744168,0.9873125,0.00001736035,0.000009423879,0.00002848725,0.00005224917,0.0002377704,0.0001570588],"genre_scores_gemma":[0.150021,0.0001164517,0.848141,0.00002757762,0.00002236339,0.0001476202,0.0005422814,0.0001050246,0.0008768192],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001807796,"threshold_uncertainty_score":0.004364967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03128598122985272,"score_gpt":0.2402468483758701,"score_spread":0.2089608671460174,"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."}}