{"id":"W1992721638","doi":"10.1515/htmp.2011.001","title":"Thermodynamic Modeling of Zinc Speciation in Electric Arc Furnace Dust","year":2011,"lang":"en","type":"article","venue":"High Temperature Materials and Processes","topic":"Metal Extraction and Bioleaching","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Zinc; Inorganic chemistry; Manganese; Electric arc furnace; Zinc ferrite; Zinc hydroxide; Oxygen; Materials science; Galvanization; Hydrogen chloride; Hydrogen; Chloride; Metallurgy; Chemistry","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":[0.0002519308,0.0003614407,0.0003688698,0.000487436,0.0003833605,0.0005234827,0.0006199733,0.0004819046,0.001055585],"category_scores_gemma":[0.000481078,0.0002732443,0.0004979636,0.0003949793,0.0003168221,0.0003523157,0.0002599566,0.0002263215,0.0002075027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001093884,"about_ca_system_score_gemma":0.0005317712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01327491,"about_ca_topic_score_gemma":0.005899293,"domain_scores_codex":[0.9999129,0.00001507826,0.000003941271,0.00002121232,0.000033445,0.00001338883],"domain_scores_gemma":[0.9998878,0.00006345674,0.000008660006,0.000006779364,0.00002650328,0.000006781058],"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":[0.0001261674,0.00008502875,0.008433644,0.00007795179,0.00002450435,0.0001787492,0.0000683761,0.9538804,0.03091172,0.001867224,0.0001079796,0.004238253],"study_design_scores_gemma":[0.00001335987,0.00003012181,0.001634794,0.000002623834,0.000004215986,0.00001653132,0.00001590149,0.9886534,0.009113492,0.0002692309,0.0002404668,0.000005964724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9715368,0.000189727,0.02254605,0.00003560978,0.000008973097,0.00005428186,0.0004140142,0.0001323038,0.005082261],"genre_scores_gemma":[0.9972844,0.00007374455,0.001674728,0.000003712423,0.000001656316,0.00002219175,0.0001136482,0.00001555524,0.0008102715],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01327491,"threshold_uncertainty_score":0.02639526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01171621136784221,"score_gpt":0.1949962100580124,"score_spread":0.1832799986901702,"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."}}