{"id":"W2002510000","doi":"10.5430/air.v1n2p131","title":"Prediction of weld quality using intelligent decision making tools","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Welding Techniques and Residual Stresses","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Computer science; Particle swarm optimization; Taguchi methods; Artificial intelligence; Process (computing); Machine learning; Field (mathematics); Genetic algorithm; Predictive modelling; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009759515,0.0007492422,0.0006044258,0.0004805422,0.000264526,0.0008802463,0.0006307617,0.0006586246,0.0009581837],"category_scores_gemma":[0.002052178,0.0003549275,0.0005894267,0.0003809669,0.0003291194,0.0008800196,0.0003428702,0.0005074288,0.0001696853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005225572,"about_ca_system_score_gemma":0.0005411539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002985182,"about_ca_topic_score_gemma":0.002295083,"domain_scores_codex":[0.9995389,0.000152071,0.00003467555,0.00008274369,0.0001552741,0.00003639516],"domain_scores_gemma":[0.9992597,0.0004495572,0.0001091085,0.00004858163,0.0001154444,0.00001764494],"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":[0.0001023132,0.00009379219,0.002249942,0.00009498272,0.00005760675,0.00006951264,0.00004955353,0.9204471,0.005918286,0.001887308,0.0002580042,0.06877147],"study_design_scores_gemma":[0.000004826673,0.00003357831,0.0003121837,0.000005650157,0.000007150476,0.000006573521,0.000005307755,0.9969509,0.001762522,0.0007776231,0.0001280592,0.000005693152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0821503,0.0003105102,0.9150143,0.0001358206,0.00003787674,0.00006895779,0.00005050519,0.0003913437,0.001840342],"genre_scores_gemma":[0.8991655,0.0003143809,0.0991931,0.00003277286,0.00001647473,0.00009501554,0.00007349216,0.00001871859,0.001090507],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002985182,"threshold_uncertainty_score":0.005935609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5156509725328996,"score_gpt":0.4893299351033655,"score_spread":0.02632103742953407,"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."}}