{"id":"W1994140784","doi":"10.1016/j.commatsci.2011.06.032","title":"Determination of volume fraction of bainite in low carbon steels using artificial neural networks","year":2011,"lang":"en","type":"article","venue":"Computational Materials Science","topic":"Microstructure and Mechanical Properties of Steels","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bainite; Volume fraction; Isothermal process; Isothermal transformation diagram; Materials science; Fraction (chemistry); Volume (thermodynamics); Carbon fibers; Metallurgy; Transformation (genetics); Thermodynamics; Microstructure; Composite material; Austenite; Chemistry; Chromatography; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001733287,0.0002062256,0.0002331245,0.000773835,0.0002285319,0.0003424287,0.0003306154,0.0003940867,0.0002548278],"category_scores_gemma":[0.0005786593,0.0001820006,0.0001130423,0.0003065878,0.0002295912,0.0003622208,0.0001219244,0.0001899783,0.00006119291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004854632,"about_ca_system_score_gemma":0.0001972943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004377279,"about_ca_topic_score_gemma":0.007576234,"domain_scores_codex":[0.9999381,0.000006947627,0.000003444849,0.00002254531,0.00002344382,0.000005487608],"domain_scores_gemma":[0.9997724,0.0001009645,0.0000490668,0.000009804969,0.00005513137,0.00001254244],"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":[0.0009466786,0.00009238652,0.04003385,0.0002173212,0.00006295882,0.0002582998,0.000112864,0.2022054,0.7021469,0.001755425,0.0002637777,0.05190411],"study_design_scores_gemma":[0.00001343703,0.00006671943,0.02898867,0.000008655088,0.00001630782,0.00006400491,0.00003193488,0.8765473,0.09308752,0.000829476,0.0003309257,0.00001515091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9718245,0.0003795883,0.02607547,0.00004064223,0.00001225313,0.000009982075,0.0001492273,0.0001139475,0.001394375],"genre_scores_gemma":[0.9956244,0.00005545803,0.004064586,0.000003090216,0.000002457188,0.000002474305,0.0000594925,0.000006362306,0.0001817293],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004377279,"threshold_uncertainty_score":0.008703589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02870783384004534,"score_gpt":0.2320877391086411,"score_spread":0.2033799052685958,"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."}}