{"id":"W4387527138","doi":"10.1016/j.apenergy.2023.122084","title":"Hydrothermal bio-char as a foaming agent for electric arc furnace steelmaking: Performance and mechanism","year":2023,"lang":"en","type":"article","venue":"Applied Energy","topic":"Metallurgical Processes and Thermodynamics","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Steelmaking; Char; Hydrothermal carbonization; Slag (welding); Foaming agent; Electric arc furnace; Hydrothermal circulation; Waste management; Materials science; Coal; Metallurgy; Chemical engineering; Carbonization; Composite material; Porosity; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001052751,0.0001758117,0.0001792796,0.0001077092,0.00009593714,0.00003008158,0.0001187172,0.00008453487,0.00002491956],"category_scores_gemma":[0.00000387069,0.0001636835,0.00004151616,0.0002974027,0.00001125647,0.00004600294,0.00003993408,0.00007960934,0.00002803826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002986427,"about_ca_system_score_gemma":0.000008983801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007011638,"about_ca_topic_score_gemma":0.00000352055,"domain_scores_codex":[0.9991417,0.000003863753,0.0001610166,0.0002073747,0.0001208928,0.0003651539],"domain_scores_gemma":[0.9997125,0.00004643779,0.00002965031,0.0001315536,0.00001152226,0.0000683916],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003559609,0.00001682132,9.449207e-7,0.0002779601,0.00009442437,0.0000071175,0.0001296899,0.04132124,0.09776988,0.7780383,0.00005247739,0.08225551],"study_design_scores_gemma":[0.0003647571,0.0000555714,0.00002192257,0.00001335963,0.00001837509,0.000007878235,0.0000256919,0.9459063,0.01986828,0.009546502,0.0239315,0.000239902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8436132,0.0005793811,0.1248216,0.00003692281,0.0001984162,0.0002377069,0.000004096357,0.0007272492,0.02978144],"genre_scores_gemma":[0.9978194,0.000797697,0.000245126,0.00008693581,0.0000924171,0.0001603507,0.00001288661,0.00006827522,0.0007169343],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.904585,"threshold_uncertainty_score":0.6674821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009297996681235508,"score_gpt":0.1967509900740819,"score_spread":0.1874529933928464,"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."}}