{"id":"W4319591698","doi":"10.1007/978-3-031-22532-1_100","title":"Mathematical Modelling of the Desulfurization of Electrolysis Cell Gases in a Low-Temperature Reactor","year":2023,"lang":"en","type":"book-chapter","venue":"˜The œminerals, metals & materials series","topic":"Industrial Gas Emission Control","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Rio Tinto (Canada); Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada; Centre québécois de recherche et de développement de l’aluminium; Graymont; Université du Québec à Chicoutimi","keywords":"Flue-gas desulfurization; Sorbent; Electrolysis; Lime; Chemistry; Relative humidity; Volumetric flow rate; Chemical engineering; Waste management; Adsorption; Materials science; Metallurgy; Thermodynamics; Electrode; Engineering; Electrolyte; Organic 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007827796,0.0005762052,0.001428343,0.0002258723,0.00005599223,0.00008388306,0.0005266003,0.0006320185,0.0004144763],"category_scores_gemma":[0.00009131352,0.0003672551,0.0002964017,0.0001902078,0.0001346221,0.0001550507,0.0001112688,0.0003942777,0.00002550985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006349706,"about_ca_system_score_gemma":0.00009310219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003586858,"about_ca_topic_score_gemma":0.0000289515,"domain_scores_codex":[0.9971564,0.0001609093,0.001484948,0.0003133245,0.0005084234,0.0003759832],"domain_scores_gemma":[0.9982047,0.000253451,0.0005589653,0.0007709158,0.0001600442,0.00005190791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000111733,0.00001915817,3.200925e-7,0.0007590341,0.0001601291,0.000005122179,0.000212862,0.02575512,0.9667525,0.005788657,0.0004208578,0.0000145632],"study_design_scores_gemma":[0.0004617128,0.00004812814,0.000002037158,0.001152981,0.0003925423,0.000009771054,0.00003492877,0.001387544,0.9849287,0.01001869,0.001138017,0.0004249461],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9597015,0.002064117,0.0007396954,0.0003977924,0.003515755,0.004146762,0.001716074,0.0006573898,0.0270609],"genre_scores_gemma":[0.8796511,0.0005622217,0.0002793113,0.00002527677,0.0005588753,0.000105933,0.0001236576,0.0003026577,0.1183909],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09133002,"threshold_uncertainty_score":0.9998779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02157118911092609,"score_gpt":0.1991126489591878,"score_spread":0.1775414598482617,"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."}}