{"id":"W4319302484","doi":"10.1007/978-3-031-22524-6_40","title":"Recent Advances in the Transformative Non-fusion Weld-Brazing Process Used to Join Thin-Gauge Alloys Used in the Automotive Industry","year":2023,"lang":"en","type":"book-chapter","venue":"The minerals, metals & materials series","topic":"Advanced Welding Techniques Analysis","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Brazing; Fusion welding; Welding; Mechanical engineering; Automotive industry; Process (computing); Materials science; Manufacturing engineering; Join (topology); Metallurgy; Engineering drawing; Computer science; Engineering; Alloy","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002723586,0.001015053,0.001472409,0.0005341137,0.0002543656,0.0003614517,0.001525094,0.0007078738,0.000248354],"category_scores_gemma":[0.0001018901,0.0006003622,0.0002310275,0.000671025,0.0001812919,0.001014875,0.0001647043,0.001330105,0.00006688024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001971649,"about_ca_system_score_gemma":0.00005934721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009541789,"about_ca_topic_score_gemma":0.002721886,"domain_scores_codex":[0.9959086,0.0003762486,0.001467854,0.0006213153,0.0008875406,0.0007384084],"domain_scores_gemma":[0.9979972,0.0004122419,0.0004212833,0.0009517182,0.0001470399,0.00007046333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001255123,0.0003970967,0.0000798193,0.005343297,0.002213089,0.0009550846,0.2660348,0.2820104,0.3836073,0.03377675,0.01128081,0.01304644],"study_design_scores_gemma":[0.002686811,0.000997989,0.0005952052,0.008205123,0.001717324,0.0001915409,0.02781739,0.001877649,0.3293332,0.1721488,0.4473096,0.007119244],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8768366,0.006994302,0.004826848,0.01531155,0.004661261,0.02077937,0.003017087,0.004843072,0.06272993],"genre_scores_gemma":[0.9499627,0.01867106,0.001994465,0.001846333,0.001028778,0.003623808,0.0007675604,0.0008956316,0.02120966],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4360288,"threshold_uncertainty_score":0.9996448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02586773549223862,"score_gpt":0.2744303722122087,"score_spread":0.2485626367199701,"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."}}