{"id":"W2896396716","doi":"10.2351/1.5059766","title":"On-line monitoring of CO2 laser welding using neural networks","year":2001,"lang":"en","type":"article","venue":"","topic":"Welding Techniques and Residual Stresses","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Queen's University","funders":"","keywords":"Welding; Automotive industry; Laser beam welding; Artificial neural network; Computer science; Laser; Robot welding; Production line; Automotive engineering; Mechanical engineering; Line (geometry); Engineering; Artificial intelligence; Optics; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002501531,0.0004152421,0.0002774143,0.0004671027,0.0001893381,0.0005368267,0.0005450168,0.0006080241,0.0005705767],"category_scores_gemma":[0.00117232,0.0001864701,0.0001270544,0.0002809616,0.0001516326,0.0004801027,0.0002436181,0.0002589962,0.0001206052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004804838,"about_ca_system_score_gemma":0.0002435552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007985014,"about_ca_topic_score_gemma":0.00886333,"domain_scores_codex":[0.999759,0.00004154456,0.00001267893,0.0000541558,0.000101995,0.00003065899],"domain_scores_gemma":[0.9995754,0.0001687289,0.00008028918,0.00002118345,0.0001355767,0.00001870464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001294062,0.0006218383,0.02265342,0.0002373566,0.0001879014,0.0003652381,0.0002289688,0.5149274,0.1126524,0.000929448,0.001911978,0.3439901],"study_design_scores_gemma":[0.000005779021,0.00006084652,0.003609403,0.000003389622,0.000008965075,0.00002381384,0.00001190331,0.9876593,0.008351136,0.0001280273,0.0001301911,0.000007332043],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8499829,0.0004271569,0.1426571,0.0002364617,0.00005538396,0.00005334388,0.0001443286,0.001273769,0.005169406],"genre_scores_gemma":[0.9949184,0.00006552245,0.004176041,0.00001525545,0.000006912222,0.00001449914,0.00004124188,0.000007555852,0.0007545479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007985014,"threshold_uncertainty_score":0.01587707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03478427548887009,"score_gpt":0.2770997984191682,"score_spread":0.2423155229302981,"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."}}