{"id":"W2950491675","doi":"","title":"Thermodynamic database of a ferro-alloys system and its application to the refining of ferromanganese alloys","year":2018,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Materials Science","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Refining (metallurgy); Ferromanganese; Metallurgy; Materials science; Manganese","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":[0.001064788,0.0004017975,0.001094062,0.004708453,0.0009230882,0.001550002,0.001932489,0.0004217966,0.005696698],"category_scores_gemma":[0.003224019,0.0003923289,0.0007684053,0.005007216,0.0003384518,0.001607317,0.0007068797,0.0003523289,0.001538914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009313102,"about_ca_system_score_gemma":0.0018379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0135048,"about_ca_topic_score_gemma":0.01928498,"domain_scores_codex":[0.9991229,0.0001171348,0.0001333383,0.0001768794,0.0004014567,0.00004819486],"domain_scores_gemma":[0.998778,0.0001787488,0.00007475679,0.0003905953,0.0005250263,0.00005272142],"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":[0.001559251,0.0009174509,0.1059308,0.001942547,0.0007840944,0.0008508517,0.0006053973,0.367121,0.04996547,0.08611642,0.03402178,0.350185],"study_design_scores_gemma":[0.00009490866,0.0001853347,0.03016416,0.00005743297,0.0002142,0.0005065303,0.0001656293,0.8453388,0.04099323,0.03259759,0.04956144,0.0001208762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6095564,0.002399281,0.2423128,0.0004858446,0.00015475,0.0003223913,0.1021492,0.01899141,0.02362781],"genre_scores_gemma":[0.8587669,0.0007508895,0.09016247,0.00004157376,0.00004693279,0.000179694,0.04593752,0.0008088946,0.003305224],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0135048,"threshold_uncertainty_score":0.02685237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0119657779343449,"score_gpt":0.2242932858499338,"score_spread":0.2123275079155889,"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."}}