{"id":"W1990684252","doi":"10.1109/iciic.2010.36","title":"Modeling of ?' Precipitate Size of IN738LC Using Levenberg&amp;#150;Marquardt Backpropagation Neural Network","year":2010,"lang":"en","type":"article","venue":"","topic":"High Temperature Alloys and Creep","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Materials science; Quenching (fluorescence); Backpropagation; Grain size; Alloy; Superalloy; Metallurgy; Artificial neural network; Computer science; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002637714,0.0004141,0.0004011494,0.0002814678,0.0002730922,0.0004678404,0.0009535222,0.0008014342,0.001460795],"category_scores_gemma":[0.0006262524,0.0003413588,0.0004069904,0.000265065,0.0003584237,0.0005118367,0.0002023355,0.0003873051,0.0002710572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001260959,"about_ca_system_score_gemma":0.0009893657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01772454,"about_ca_topic_score_gemma":0.01559132,"domain_scores_codex":[0.9999082,0.00001462737,0.000004743913,0.00002595431,0.00003163995,0.00001487674],"domain_scores_gemma":[0.9997781,0.00007640128,0.00004449033,0.00001196287,0.000079044,0.00001002491],"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":[0.00001130419,0.00001026071,0.0004242208,0.00001651455,0.000005188616,0.00002533428,0.00001065108,0.9939749,0.002495468,0.0004612455,0.0001347004,0.002430285],"study_design_scores_gemma":[5.710086e-7,0.000001880145,0.0000696952,4.528419e-7,5.35008e-7,0.000002067572,6.145186e-7,0.9994572,0.0003661287,0.00005818472,0.0000418187,9.32294e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.318832,0.0003348169,0.6665959,0.0003695795,0.00006911776,0.00007906801,0.0003599808,0.001452543,0.01190701],"genre_scores_gemma":[0.9587188,0.0001457011,0.03272248,0.00003540979,0.000008172923,0.00006925136,0.0001521097,0.00007860659,0.008069598],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01772454,"threshold_uncertainty_score":0.03524274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01458626416109488,"score_gpt":0.2223371318679718,"score_spread":0.207750867706877,"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."}}