{"id":"W2085060307","doi":"10.4028/www.scientific.net/ddf.258-260.282","title":"Numerical Model to Describe the Growth of the Internal Oxidation Layer in Binary Alloys","year":2006,"lang":"en","type":"article","venue":"Defect and diffusion forum/Diffusion and defect data, solid state data. Part A, Defect and diffusion forum","topic":"High-Temperature Coating Behaviors","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Comisión de Operación y Fomento de Actividades Académicas, Instituto Politécnico Nacional","keywords":"Binary number; Layer (electronics); Thermodynamics; Materials science; Set (abstract data type); Internal oxidation; Statistical physics; Mechanics; Mathematics; Metallurgy; Computer science; Physics; Nanotechnology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009948509,0.0008761604,0.0009077487,0.0004596067,0.0008462207,0.0003440059,0.001274701,0.0003581992,0.00002961587],"category_scores_gemma":[0.0002720791,0.0005784623,0.0002667196,0.0008405457,0.0003073797,0.000812361,0.003937263,0.0008025089,0.00001783873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006962573,"about_ca_system_score_gemma":0.00006433487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001144355,"about_ca_topic_score_gemma":0.002711335,"domain_scores_codex":[0.9952195,0.000339536,0.001174732,0.001337577,0.0008175795,0.001111061],"domain_scores_gemma":[0.9966744,0.0004414892,0.0002656777,0.002091657,0.0001123334,0.0004144704],"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":[0.001056913,0.001512602,0.3000789,0.0005573925,0.0001820327,0.00007769582,0.002496026,0.002867682,0.5654876,0.001306478,0.116141,0.008235695],"study_design_scores_gemma":[0.01558676,0.001681969,0.2911499,0.002469985,0.0012696,0.0005069271,0.002544851,0.6007026,0.02156978,0.004181257,0.05302665,0.005309755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915801,0.001289563,0.002645924,0.0004674073,0.000660615,0.001230886,0.001696468,0.0002254094,0.0002035956],"genre_scores_gemma":[0.9949354,0.00141705,0.0002749527,0.0004699301,0.0001058505,0.00006790548,0.002414639,0.0001213147,0.000192935],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5978349,"threshold_uncertainty_score":0.9996667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02201019923159479,"score_gpt":0.2613385528796779,"score_spread":0.2393283536480831,"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."}}