{"id":"W2045890980","doi":"10.1177/0892705711428657","title":"Optimization of thermoplastic composites resistance welding parameters based on transient heat transfer finite element modeling","year":2011,"lang":"en","type":"article","venue":"Journal of Thermoplastic Composite Materials","topic":"Thermal properties of materials","field":"Materials Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; École de Technologie Supérieure; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Composite material; Welding; Finite element method; Clamping; Heat transfer; Overheating (electricity); Thermal resistance; Thermal conduction; Electric resistance welding; Thermoplastic; Structural engineering; Mechanical engineering; Mechanics","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001471853,0.0004700347,0.001073031,0.0002969131,0.000164813,0.0001620603,0.0007300051,0.0001361713,0.003075592],"category_scores_gemma":[0.0001236382,0.0003611167,0.0002307815,0.0001191681,0.0001537398,0.0003468504,0.00005584579,0.0001466799,0.00003354394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001283079,"about_ca_system_score_gemma":0.00008749037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003578717,"about_ca_topic_score_gemma":0.00000227719,"domain_scores_codex":[0.9956861,0.0006517479,0.00200022,0.0003691741,0.000792891,0.0004999352],"domain_scores_gemma":[0.9980651,0.0005430086,0.0004440903,0.0004290283,0.0003418534,0.000176946],"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":[0.00444332,0.0001944919,0.000009898142,0.0001532125,0.00004328815,0.00001649071,0.0004881361,0.4803412,0.5142747,0.0000198774,0.000006220788,0.000009167758],"study_design_scores_gemma":[0.001705978,0.0008992983,0.0001144749,0.001089666,0.000235634,0.00002162964,0.00007167119,0.05682075,0.9386251,0.00003949559,0.000008558864,0.0003677616],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8292422,0.00006318223,0.1684216,0.00006817731,0.00141137,0.0004427042,0.0001191302,0.00004263676,0.0001890052],"genre_scores_gemma":[0.9807458,0.00001652609,0.01889211,0.0001220857,0.0001093937,0.00001707473,0.000008270714,0.00007920009,0.000009565235],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4243504,"threshold_uncertainty_score":0.9998841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03159066905244168,"score_gpt":0.2167811035999914,"score_spread":0.1851904345475497,"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."}}