{"id":"W4230155131","doi":"10.32920/ryerson.14656635","title":"Microstructure and Mechanical Properties of Welded Advanced Materials for Automotive Applications","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Welding Techniques Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; International Zinc Association; University of Waterloo; ArcelorMittal","keywords":"Materials science; Welding; Ultimate tensile strength; Composite material; Microstructure; Metallurgy; Spot welding; Adhesive; Layer (electronics)","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":[0.00008568934,0.0002191291,0.0005045938,0.00006913765,0.00002881456,0.00003753616,0.0001678018,0.0002616326,0.00003426649],"category_scores_gemma":[0.00004792152,0.000198515,0.00009585795,0.00006959126,0.00003739697,0.00004579641,0.0002768178,0.0001531836,2.063839e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006464688,"about_ca_system_score_gemma":0.00002432728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001061972,"about_ca_topic_score_gemma":0.0000110711,"domain_scores_codex":[0.9990973,0.00001222763,0.0003428202,0.0003145717,0.00009151641,0.0001415147],"domain_scores_gemma":[0.999197,0.00002209047,0.00008908491,0.0003936871,0.000263285,0.00003485952],"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":[0.000007994462,0.000007020049,5.102542e-7,0.001063827,0.000131809,2.175866e-7,0.00006652864,0.008896744,0.987388,0.000732909,0.00004684526,0.001657577],"study_design_scores_gemma":[0.00009931891,0.00001019698,0.000005464298,0.0001749162,0.00009402039,0.000002176874,0.00009284445,0.003739135,0.9904794,0.004802157,0.0002924706,0.0002078762],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1515742,0.000777958,0.845323,0.00006583476,0.0001069817,0.001294111,0.0001997175,0.0005988198,0.00005940254],"genre_scores_gemma":[0.7194826,0.0003011167,0.2792622,0.00001132413,0.00002944136,0.0007642054,0.0000708976,0.0000412668,0.00003692067],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5679084,"threshold_uncertainty_score":0.8095205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01142829054491356,"score_gpt":0.2442701146567246,"score_spread":0.2328418241118111,"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."}}