{"id":"W4254439412","doi":"10.1002/9781119328827.ch17","title":"On Control of Grain Coarsening of Austenite by Nano-Scale Precipitate Engineering of TiN-NbC Composite in Ti-Nb Microalloyed Steel","year":2016,"lang":"en","type":"book-chapter","venue":"","topic":"Metal Alloys Wear and Properties","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Companhia Brasileira de Metalurgia e Mineração","keywords":"Tin; Materials science; Composite number; Metallurgy; Austenite; Microalloyed steel; Nanoscopic scale; Nano-; Scale (ratio); Composite material; Nanotechnology; Microstructure; Geography","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00057402,0.0003625316,0.001064963,0.0002361489,0.00001840212,0.00001389833,0.0003557177,0.0002266215,0.0006923978],"category_scores_gemma":[0.00003377823,0.0002585408,0.0001902308,0.00003342026,0.0001870589,0.00008533237,0.0001036191,0.0001567616,0.00005725033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003249672,"about_ca_system_score_gemma":0.00003846301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008390228,"about_ca_topic_score_gemma":0.00002946615,"domain_scores_codex":[0.9978951,0.00006473468,0.0009968274,0.0003603055,0.0003986858,0.0002843367],"domain_scores_gemma":[0.9986079,0.0002884016,0.0005149415,0.000367211,0.0001502658,0.00007124286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003473155,0.0000345423,0.00003912075,0.0003468075,0.00005106736,0.000002069403,0.0002061725,0.0001216088,0.9932728,0.005158434,0.000203316,0.0002167551],"study_design_scores_gemma":[0.001449818,0.0004007385,0.00004401257,0.001667891,0.00006428659,0.000003404489,0.00001203369,0.0002114589,0.9920025,0.0003781098,0.003391637,0.0003740718],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7370766,0.001707649,0.002073973,0.0001082664,0.001041194,0.001676263,0.001693253,0.0001153083,0.2545075],"genre_scores_gemma":[0.7943955,0.0000496239,0.001267708,0.0000350526,0.00002650124,0.000009453608,0.00001444741,0.00006401139,0.2041377],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05731892,"threshold_uncertainty_score":0.9999867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009286549678222381,"score_gpt":0.1905157031584207,"score_spread":0.1812291534801983,"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."}}