{"id":"W2531084657","doi":"10.4236/jsemat.2016.64014","title":"A Predictive Modeling Based on Regression and Artificial Neural Network Analysis of Laser Transformation Hardening for Cylindrical Steel Workpieces","year":2016,"lang":"en","type":"article","venue":"Journal of Surface Engineered Materials and Advanced Technology","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cégep de Rimouski; Université du Québec à Rimouski","funders":"","keywords":"Materials science; Hardening (computing); Artificial neural network; Laser; Response surface methodology; Martensite; Nonlinear system; Mechanical engineering; Composite material; Metallurgy; Computer science; Optics; Microstructure; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.000200061,0.0001534265,0.0004931107,0.0002552402,0.00004368257,0.00001990468,0.00006491598,0.0001327491,0.00001263319],"category_scores_gemma":[0.00006175994,0.0001014466,0.00004899104,0.000174011,0.00003901177,0.0001689279,0.00001104039,0.00006996543,7.627379e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001735935,"about_ca_system_score_gemma":0.000007191489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.149081e-7,"about_ca_topic_score_gemma":5.59803e-7,"domain_scores_codex":[0.9991394,0.00001594929,0.0004645705,0.0001099492,0.00009356854,0.00017659],"domain_scores_gemma":[0.9994859,0.0001510213,0.0001608714,0.00006875549,0.00009465019,0.00003880464],"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":[0.0003318444,0.000007663364,0.00001588316,0.00008219761,0.00009984846,0.000001165678,0.00002891715,0.7521899,0.2426205,0.00009113918,0.000002837195,0.004528055],"study_design_scores_gemma":[0.0007447265,0.0003418858,0.0001953468,0.0004232304,0.0002269835,0.000004128869,0.00007437184,0.2965528,0.6999916,0.001226004,0.00006886919,0.0001500049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8102632,0.0002911586,0.1888266,0.0001425393,0.000219227,0.0001014894,0.0001043767,0.000048312,0.000003097991],"genre_scores_gemma":[0.9918583,0.0003254373,0.007714676,0.000004313373,0.00006522544,0.000006517709,0.000005653543,0.00001846005,0.000001441846],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4573711,"threshold_uncertainty_score":0.4136873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01013263242551391,"score_gpt":0.2237204089763305,"score_spread":0.2135877765508166,"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."}}