{"id":"W4296369020","doi":"10.1177/0309524x221123324","title":"Development of a cross-sectional finite element for the analysis of thin-walled composite beams like wind turbine blades","year":2022,"lang":"en","type":"article","venue":"Wind Engineering","topic":"Composite Structure Analysis and Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Image warping; Finite element method; Quadrilateral; Discretization; Computation; Beam (structure); Structural engineering; Cross section (physics); Degrees of freedom (physics and chemistry); Turbine blade; Turbine; Engineering; Mathematics; Mathematical analysis; Mechanical engineering; Computer science; Physics; Algorithm","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002419582,0.0001536347,0.0003019345,0.0003500855,0.0001599539,0.00002911409,0.0002037841,0.00003262009,0.0001621658],"category_scores_gemma":[0.000008831144,0.000136306,0.0002322299,0.0008304105,0.0000134281,0.00005488636,0.00007580093,0.0001257241,2.450655e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000109176,"about_ca_system_score_gemma":0.00001677596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008285619,"about_ca_topic_score_gemma":0.000009163223,"domain_scores_codex":[0.99886,0.000007782583,0.0005156861,0.0001513361,0.0002923622,0.0001728567],"domain_scores_gemma":[0.9994668,0.0001415433,0.00008819992,0.0001957037,0.00007596271,0.00003181283],"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":[0.00001837455,0.00001663981,0.003278891,0.00005329173,0.002313404,2.670656e-7,0.0009234684,0.9790196,0.01399911,0.00005211314,0.0000109396,0.0003138289],"study_design_scores_gemma":[0.00026929,0.00001554991,0.09276123,0.000005490463,0.0003756834,7.712755e-7,0.00004425237,0.9006597,0.00335108,0.000003216067,0.002381349,0.0001324107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7744656,0.0004277782,0.2244445,0.00001023932,0.0002596715,0.0002131879,0.000070109,0.00006116044,0.00004779226],"genre_scores_gemma":[0.983311,0.000009364445,0.01634449,0.000008763815,0.00004298968,0.00002399462,0.0002055649,0.00002327264,0.00003055282],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2088455,"threshold_uncertainty_score":0.5558397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008981536082496792,"score_gpt":0.2211841690674603,"score_spread":0.2122026329849635,"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."}}