{"id":"W2897223499","doi":"10.1007/s12666-018-1435-4","title":"Prediction and Verification of Effective Grain Refiners for Magnesium Alloys","year":2018,"lang":"en","type":"article","venue":"Transactions of the Indian Institute of Metals","topic":"Aluminum Alloy Microstructure Properties","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Materials science; Magnesium; Metallurgy","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":[0.0007388411,0.0007611753,0.0007564645,0.001083813,0.0004226859,0.0009535921,0.001189211,0.0008585296,0.001069858],"category_scores_gemma":[0.002420519,0.0004986495,0.0005107214,0.0003777973,0.0004703917,0.0006193482,0.0003916891,0.000407501,0.0004031298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001159055,"about_ca_system_score_gemma":0.001610484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02160802,"about_ca_topic_score_gemma":0.02122732,"domain_scores_codex":[0.9996064,0.0000523682,0.00002506231,0.00009826232,0.00015255,0.00006545607],"domain_scores_gemma":[0.9988171,0.000447135,0.0001747723,0.000142795,0.0003721105,0.00004615794],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002502594,0.00009244675,0.02043552,0.00007630346,0.0000246175,0.0001287665,0.00003520111,0.939818,0.01715831,0.001451256,0.0004888391,0.02004052],"study_design_scores_gemma":[0.000006209707,0.00002614831,0.002147102,0.000001871104,0.000003320123,0.000008880942,0.000008457994,0.9927636,0.004662908,0.0002665668,0.0001020233,0.00000295497],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8343309,0.0005135863,0.1593257,0.0001313433,0.00003396021,0.00009074253,0.0007561799,0.002174876,0.002642801],"genre_scores_gemma":[0.987878,0.00003910463,0.01139555,0.000003554446,0.000002301281,0.00001125498,0.0002898864,0.00003911843,0.0003412929],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02160802,"threshold_uncertainty_score":0.04296452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01080558549886792,"score_gpt":0.2067671804859445,"score_spread":0.1959615949870766,"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."}}