{"id":"W3089717279","doi":"10.3390/cryst10100901","title":"Numerical Simulation of Melting Kinetics of Metal Particles during Tapping with Argon-Bottom Stirring","year":2020,"lang":"en","type":"article","venue":"Crystals","topic":"Metallurgical Processes and Thermodynamics","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"University of Toronto; Consejo Nacional de Ciencia y Tecnología","keywords":"Ladle; Materials science; Superheating; Argon; Metallurgy; Particle (ecology); Kinetics; Diffusion; Melting point; Composite material; Thermodynamics; Chemistry; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005304223,0.0001186311,0.000283836,0.00002810745,0.00001819268,0.00001072571,0.0000810945,0.00003899406,0.00002741781],"category_scores_gemma":[0.00007510878,0.0001002029,0.0000529605,0.0002327408,0.00003192883,0.0000932058,0.0000256074,0.00009420051,0.000001323906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001149915,"about_ca_system_score_gemma":0.000005044801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002649061,"about_ca_topic_score_gemma":3.315118e-7,"domain_scores_codex":[0.9991924,0.00001412544,0.0003388529,0.0001209438,0.0001712545,0.0001624529],"domain_scores_gemma":[0.9995924,0.00008434913,0.00009827531,0.000097052,0.00005144773,0.0000764791],"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":[0.00001717106,0.00001051874,0.0001001921,0.000407984,0.0000424757,0.000002511787,0.0002100925,0.6938949,0.3049866,0.00009503294,9.080641e-8,0.0002324431],"study_design_scores_gemma":[0.0002557139,0.00005013754,0.000733538,0.00007026944,0.00003363409,0.000001297889,0.00009607248,0.8965003,0.1020551,0.00007016224,0.00002358713,0.0001101943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7028161,0.0001562041,0.296639,0.00001219404,0.00001312794,0.00006352217,0.000003164822,0.00006435029,0.0002323997],"genre_scores_gemma":[0.9975035,0.000008086678,0.002408628,0.00000571577,0.000038688,0.000002352542,0.000002262917,0.00002885074,0.000001919652],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2946874,"threshold_uncertainty_score":0.4086156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01688253753965829,"score_gpt":0.2182698348706609,"score_spread":0.2013872973310026,"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."}}