{"id":"W2802014297","doi":"10.3390/coatings8060226","title":"ANN Laser Hardening Quality Modeling Using Geometrical and Punctual Characterizing Approaches","year":2018,"lang":"en","type":"article","venue":"Coatings","topic":"Laser Material Processing Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Rimouski","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hardening (computing); Case hardening; Materials science; Laser; Laser power scaling; Mechanical engineering; Process (computing); Laser scanning; Process variable; Hardness; Computer science; Optics; Composite material; Engineering; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004713902,0.0001919782,0.0002429513,0.0001378864,0.0001769849,0.000221917,0.0001343332,0.0001154788,0.00001764266],"category_scores_gemma":[0.0002046454,0.0001873984,0.00002884144,0.0002377077,0.0000876176,0.0003744663,0.000135365,0.000156883,0.000007831307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005746097,"about_ca_system_score_gemma":0.00001318485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003635518,"about_ca_topic_score_gemma":0.00000145626,"domain_scores_codex":[0.998924,0.00002950345,0.0003155452,0.000260282,0.0001641432,0.0003065413],"domain_scores_gemma":[0.9995803,0.00004279071,0.00005746647,0.0001718295,0.00006572893,0.00008188874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005415515,0.00003972072,0.003007572,0.0008526173,0.00005448166,0.000007824332,0.004560729,0.003413138,0.9693635,0.0002086889,0.00028538,0.01815219],"study_design_scores_gemma":[0.000179436,0.00003355874,0.0002733511,0.0001154544,0.00001688141,0.00001500237,0.000135188,0.8286618,0.169533,0.0004427753,0.0002146469,0.0003788698],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.889965,0.00006131537,0.1084008,0.00002757243,0.000151267,0.00008394121,0.000005152259,0.0008212584,0.0004837085],"genre_scores_gemma":[0.9632678,0.000004544743,0.0362175,0.00006426042,0.0003657728,0.000008041841,0.00000705009,0.00005199432,0.00001303882],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8252487,"threshold_uncertainty_score":0.7641884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1281454341745664,"score_gpt":0.2843279079117547,"score_spread":0.1561824737371883,"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."}}