{"id":"W2063610829","doi":"10.1117/12.417390","title":"&lt;title&gt;Near-IR optical process sensor for electric arc furnace pollution control and energy efficiency&lt;/title&gt;","year":2001,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Spectroscopy and Laser Applications","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Electric arc furnace; Electric arc; Calibration; Exhaust gas; Analytical Chemistry (journal); Materials science; Methane; Process control; Chemistry; Process (computing); Metallurgy; Electrode; Physics; Computer science","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":[0.0003343124,0.0003636927,0.0003718619,0.000410948,0.0003426596,0.0009405504,0.0008640309,0.001124344,0.006479408],"category_scores_gemma":[0.0002866671,0.0002169369,0.0002166452,0.0003586684,0.0004383296,0.0008891142,0.0002547722,0.0006156526,0.003151597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000830373,"about_ca_system_score_gemma":0.0005738892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000991831,"about_ca_topic_score_gemma":0.001947025,"domain_scores_codex":[0.9996011,0.0000375291,0.00001250656,0.00007749389,0.0002418807,0.0000294834],"domain_scores_gemma":[0.9997001,0.00001949328,0.00003699239,0.00002314973,0.0001967553,0.00002362362],"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":[0.000327933,0.0002016049,0.0025625,0.0003704258,0.00002942062,0.0001824641,0.00003992909,0.001729925,0.8031412,0.005940754,0.02293694,0.1625369],"study_design_scores_gemma":[0.00003549303,0.0004309134,0.003955185,0.00002329464,0.00001900206,0.0004033282,0.00004241682,0.01626267,0.9192062,0.0003609279,0.05921743,0.00004315366],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3029897,0.01052978,0.5266113,0.005001543,0.003015256,0.0009592205,0.002483229,0.01108374,0.1373262],"genre_scores_gemma":[0.5799053,0.002799421,0.2765542,0.00150438,0.0003071155,0.0002772428,0.001491222,0.0003116792,0.1368494],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006479408,"threshold_uncertainty_score":0.02167577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006015255561274302,"score_gpt":0.2251982367551494,"score_spread":0.2191829811938751,"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."}}