{"id":"W3005497310","doi":"10.1109/tasc.2020.2971456","title":"Critical Temperature Prediction for a Superconductor: A Variational Bayesian Neural Network Approach","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Applied Superconductivity","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Institut de Valorisation des Données; Canada First Research Excellence Fund","keywords":"Artificial intelligence; Machine learning; Interpretability; Artificial neural network; Computer science; Inference; Context (archaeology)","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":[0.001432867,0.0005834742,0.001024778,0.001083844,0.0004596087,0.0008789599,0.002045234,0.001451564,0.001448293],"category_scores_gemma":[0.004082719,0.0007151824,0.0007092235,0.0006903516,0.001071278,0.001733743,0.0007758644,0.001393366,0.000196291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00149576,"about_ca_system_score_gemma":0.001528564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01610133,"about_ca_topic_score_gemma":0.01352818,"domain_scores_codex":[0.999622,0.0001651046,0.00001384115,0.00008134986,0.00007632229,0.00004149603],"domain_scores_gemma":[0.9988589,0.0008233174,0.00008183887,0.00002766128,0.00015653,0.000051752],"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":[0.00003478252,0.00002047934,0.001252425,0.00004331629,0.00002726409,0.00005331431,0.00003574905,0.960928,0.0005018963,0.02759168,0.0007101172,0.008801033],"study_design_scores_gemma":[0.000001725806,0.000001503832,0.00004748725,0.000002444221,0.000001659228,0.00000294761,0.000001457598,0.994563,0.0000416269,0.005275363,0.00005840024,0.000002380648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0613157,0.001120125,0.9319014,0.001413179,0.00006530603,0.00005011926,0.0001906322,0.0002101969,0.003733432],"genre_scores_gemma":[0.8481945,0.001350747,0.1444788,0.0002982124,0.000182705,0.0001595966,0.000482815,0.0001405416,0.004712169],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01610133,"threshold_uncertainty_score":0.0320152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02664870983476842,"score_gpt":0.2592076375575089,"score_spread":0.2325589277227404,"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."}}