{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002602688,0.0001303869,0.0001979886,0.0002934218,0.0001607244,0.00007509829,0.0003019206,0.0001549612,0.0002404021],"category_scores_gemma":[0.0006870002,0.0001191333,0.00008077531,0.0006748512,0.0001641514,0.000325076,0.000148823,0.0004111273,0.0000573278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001597517,"about_ca_system_score_gemma":0.00002654041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001660209,"about_ca_topic_score_gemma":0.00002279977,"domain_scores_codex":[0.9977543,0.0001416677,0.0006240979,0.0001769773,0.000683962,0.0006189328],"domain_scores_gemma":[0.9984639,0.0008081516,0.00004572212,0.0003386145,0.000226645,0.0001169495],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001039085,0.0002496833,0.005187536,0.0002659629,0.0000516108,0.000003565245,0.001248213,0.0450198,0.09304307,0.01803572,0.0006429827,0.836148],"study_design_scores_gemma":[0.000008005622,0.0000669105,0.00119475,0.000313172,0.000006712498,0.00000446249,0.0009114373,0.01901942,0.959398,0.01809129,0.0008400019,0.0001458319],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6595632,0.0007678408,0.3373554,0.00001760754,0.0004154774,0.0002509082,0.00001955131,0.0001755857,0.001434363],"genre_scores_gemma":[0.9903808,0.0003864953,0.008863661,0.000003154819,0.0003138924,0.00001480151,0.000003140932,0.00002705086,0.00000702804],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8663549,"threshold_uncertainty_score":0.4858114,"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."}}