{"id":"W4220906241","doi":"10.1111/jiec.13268","title":"Regional analysis of aluminum and steel flows into the American automotive industry","year":2022,"lang":"en","type":"article","venue":"Journal of Industrial Ecology","topic":"Extraction and Separation Processes","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Automotive industry; Scrap; Material flow analysis; Upstream (networking); Raw material; Environmental science; Metallurgy; Materials science; Engineering; Waste management","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003261763,0.0002145567,0.0001319873,0.002449419,0.0003149423,0.0006495441,0.0002401982,0.0001657239,0.0008236957],"category_scores_gemma":[0.0007615674,0.0001349536,0.0003750554,0.002155695,0.0002011867,0.0002898158,0.000364339,0.0002338199,0.0001548946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001411206,"about_ca_system_score_gemma":0.001301923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2081324,"about_ca_topic_score_gemma":0.2119677,"domain_scores_codex":[0.9998171,0.00003778496,0.000007313551,0.00005603776,0.00004906497,0.00003276084],"domain_scores_gemma":[0.9994261,0.00008795771,0.0001222487,0.00002855833,0.0002998668,0.00003529982],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001004827,0.00005835486,0.951519,0.00004836943,0.0001272338,0.0002120735,0.0009167967,0.01461382,0.007539735,0.001657223,0.000884189,0.02232287],"study_design_scores_gemma":[0.000005465026,0.00003632778,0.972181,0.00001788473,0.00005898374,0.00006073925,0.001588391,0.01922512,0.002393916,0.0003176188,0.004100238,0.00001421988],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942003,0.00006595301,0.001430142,0.00004470335,0.000001638445,0.00002334446,0.001219469,0.00003339669,0.002981036],"genre_scores_gemma":[0.9952807,0.0001214166,0.002172899,0.00001654364,0.000002868028,0.00002086275,0.001337018,0.00000941351,0.00103825],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2081324,"threshold_uncertainty_score":0.4138418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02803977315093052,"score_gpt":0.2773037484175676,"score_spread":0.249263975266637,"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."}}