{"id":"W3103807753","doi":"10.1007/s40831-020-00310-6","title":"Estimation of Net Carbon Consumption in Aluminum Electrolysis Using Multivariate Analysis","year":2020,"lang":"en","type":"article","venue":"Journal of Sustainable Metallurgy","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Alcoa (Canada); Université Laval; Centre de Recherche en Sciences Animales de Deschambault","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Anode; Carbon footprint; Process engineering; Carbon fibers; Multivariate statistics; Computer science; Electrolytic process; Volume (thermodynamics); Environmental science; Electrolysis; Raw material; Greenhouse gas; Engineering; Chemistry; Algorithm; Electrode","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":[0.0002184567,0.0003513017,0.0003259267,0.0005434554,0.000218195,0.0003381878,0.000256602,0.0002547087,0.0004659931],"category_scores_gemma":[0.0005618792,0.000117805,0.0003359834,0.0007018198,0.0001137929,0.0002973271,0.0001603693,0.0002395875,0.0001121165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002929315,"about_ca_system_score_gemma":0.0001839308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002860538,"about_ca_topic_score_gemma":0.003948648,"domain_scores_codex":[0.9998658,0.00002360753,0.000006037113,0.00002892946,0.00006448504,0.00001109571],"domain_scores_gemma":[0.9998202,0.00008981374,0.00002561235,0.00001701954,0.00004153672,0.000005930111],"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":[0.002136881,0.0004900912,0.1037956,0.0002924557,0.000288734,0.0002045878,0.0001186503,0.1950304,0.4996714,0.001470011,0.0005639296,0.1959373],"study_design_scores_gemma":[0.00001089539,0.0002146762,0.05289019,0.000004581775,0.00004265557,0.00006713807,0.00005334851,0.7883273,0.1576033,0.0004215374,0.0003354398,0.0000288719],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.977219,0.00009340948,0.02144979,0.00002585959,0.000007794899,0.000008861971,0.0002347958,0.0001007313,0.0008596823],"genre_scores_gemma":[0.9961654,0.00003815879,0.003381828,0.000002323888,0.000002113518,0.000006438596,0.00007592227,0.000007177746,0.0003206165],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002860538,"threshold_uncertainty_score":0.005687773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02160213082985769,"score_gpt":0.2897462136644664,"score_spread":0.2681440828346087,"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."}}