{"id":"W4394999206","doi":"10.1088/1742-6596/2738/1/012020","title":"Technology Selection for Slag Zinc Fuming Process","year":2024,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Recycling and Waste Management Techniques","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"SNC-Lavalin (Canada); École de Technologie Supérieure; University of Toronto","funders":"","keywords":"Slag (welding); Zinc; Selection (genetic algorithm); Process (computing); Environmental science; Process engineering; Waste management; Metallurgy; Materials science; Computer science; Engineering; Artificial intelligence","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":[0.0001915953,0.00008643271,0.0001283444,0.00006337537,0.00007624907,0.0001015903,0.0001750335,0.00004571718,0.00004731742],"category_scores_gemma":[0.00002363836,0.00007058039,0.00005893479,0.0002940581,0.00009968454,0.0006129773,0.00004120842,0.0001500966,0.0000106886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000468675,"about_ca_system_score_gemma":0.00002674312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003562817,"about_ca_topic_score_gemma":0.000005090574,"domain_scores_codex":[0.9993967,0.00001008422,0.0001925198,0.0001175515,0.0001518244,0.00013127],"domain_scores_gemma":[0.9997335,0.00001992704,0.0001147942,0.0000599637,0.00004813907,0.00002365296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004305606,0.00005201743,0.002679086,0.0001650817,0.00005595651,0.00000444739,0.0006422389,0.0004891583,0.05542685,0.05156643,0.003693458,0.8851822],"study_design_scores_gemma":[0.0001261807,0.0006306953,0.0001804052,0.000271827,0.00006045218,0.00004530384,0.0008085102,0.003345483,0.4948128,0.4599468,0.03956825,0.0002033346],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.342671,0.0001141672,0.6465378,0.002290093,0.0009040973,0.0002965672,0.00000337568,0.0002640364,0.006918825],"genre_scores_gemma":[0.9933414,0.00006439473,0.005641252,0.00001903174,0.0002541755,0.00001132962,6.209317e-7,0.0000105745,0.0006571771],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8849789,"threshold_uncertainty_score":0.2878185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01563093738280788,"score_gpt":0.2765891412265093,"score_spread":0.2609582038437014,"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."}}