{"id":"W2158631537","doi":"10.5430/air.v1n1p46","title":"CVD and PVD coating process modelling by using artificial neural networks","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Metal and Thin Film Mechanics","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Materials science; Coating; Physical vapor deposition; Chemical vapor deposition; Artificial neural network; Layer (electronics); Deposition (geology); Titanium; Thin film; Process (computing); Multilayer perceptron; Vapour deposition; Composite material; Metallurgy; Computer science; Artificial intelligence; Nanotechnology","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":[0.00199097,0.0002076324,0.0002253659,0.0001608374,0.0004291803,0.0001980589,0.0002422368,0.0001679349,0.00008088369],"category_scores_gemma":[0.0001529783,0.000206346,0.00004739229,0.0006721307,0.0001389742,0.0004596251,0.0001067121,0.0008232795,0.00005665506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006206425,"about_ca_system_score_gemma":0.00001958304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001059466,"about_ca_topic_score_gemma":0.00001259425,"domain_scores_codex":[0.9973966,0.000136401,0.0004686191,0.0002854374,0.0005593715,0.001153585],"domain_scores_gemma":[0.999087,0.0001681065,0.00004591564,0.0002161219,0.0001689501,0.0003138897],"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":[0.0000423019,0.0001132996,0.0001288207,0.0001402628,0.00003444732,0.000006321142,0.001614082,0.7895347,0.05869584,0.03023624,0.0001146158,0.1193391],"study_design_scores_gemma":[0.000007725014,0.00002754504,4.97074e-7,0.00002613461,0.000007294609,0.000007779354,0.0009762301,0.8563736,0.1297536,0.01255703,0.00006900947,0.0001935163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6140923,0.001355143,0.3831075,0.00002930259,0.0007296024,0.0002203768,0.000004707052,0.0001122689,0.0003488119],"genre_scores_gemma":[0.9982283,0.0001123582,0.0008954139,0.00001446544,0.0006350387,0.00002168743,0.000008328267,0.00005484594,0.00002961203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3841359,"threshold_uncertainty_score":0.8414544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2599747225708484,"score_gpt":0.3884304723038766,"score_spread":0.1284557497330282,"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."}}