{"id":"W2004830604","doi":"10.1016/j.apm.2012.06.018","title":"Modeling of thermotransport phenomenon in metal alloys using artificial neural networks","year":2012,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Nuclear reactor physics and engineering","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Soundness; Artificial neural network; Computer science; Binary number; Phenomenon; Field (mathematics); Artificial intelligence; Biological system; Materials science; Mathematics; Physics","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":[0.0002396958,0.0003383046,0.0004701808,0.0002696041,0.0003725745,0.0005756055,0.0007762953,0.00083537,0.001081732],"category_scores_gemma":[0.0006585842,0.000320723,0.0004389997,0.0003586532,0.0005383126,0.0007877632,0.0002766035,0.0004213718,0.0001347683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006980873,"about_ca_system_score_gemma":0.0005558287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007195545,"about_ca_topic_score_gemma":0.005739458,"domain_scores_codex":[0.9999228,0.00002146638,0.000005062615,0.00001604963,0.00002279834,0.00001193471],"domain_scores_gemma":[0.9997949,0.0001091692,0.00003171714,0.00001190444,0.00004220533,0.00001009719],"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":[0.00002046172,0.00001571337,0.0002330753,0.0000255574,0.00001033457,0.00003314114,0.00001318276,0.993201,0.00227468,0.002448555,0.00007386841,0.001650474],"study_design_scores_gemma":[7.927692e-7,0.000001955808,0.00003522932,6.794003e-7,0.000001060711,0.000001922939,0.000001018656,0.9995002,0.0001773914,0.0002416608,0.00003714352,0.000001021213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.544552,0.002414191,0.4240567,0.0008484162,0.0002724292,0.00007796888,0.0001856186,0.0004161909,0.02717639],"genre_scores_gemma":[0.9899454,0.0004131754,0.005711145,0.00002992509,0.00001975812,0.0000314809,0.00003920968,0.00002001678,0.003790077],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.007195545,"threshold_uncertainty_score":0.01430732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04015747892616803,"score_gpt":0.2132174053767724,"score_spread":0.1730599264506044,"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."}}