{"id":"W2971631894","doi":"10.1088/1755-1315/323/1/012056","title":"Enhancing consistency in consequential life cycle inventory through material flow analysis","year":2019,"lang":"en","type":"article","venue":"IOP Conference Series Earth and Environmental Science","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Université de Sherbrooke; Natural Sciences and Engineering Research Council of Canada","funders":"","keywords":"Supply chain; Material flow; Material flow analysis; Greenhouse gas; Computer science; Consistency (knowledge bases); Extrapolation; Environmental science; Environmental economics; Operations research; Business; Economics; Engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.005954667,0.0007839406,0.0008002521,0.002975631,0.0005386301,0.001703653,0.001265316,0.0006604204,0.002150713],"category_scores_gemma":[0.01354506,0.0005606824,0.001476711,0.001811051,0.0005012863,0.001921607,0.001064946,0.0008138023,0.0002167483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002480946,"about_ca_system_score_gemma":0.002883404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07295216,"about_ca_topic_score_gemma":0.05044312,"domain_scores_codex":[0.998494,0.0006188787,0.0001104103,0.0002005678,0.0004461367,0.0001299066],"domain_scores_gemma":[0.991163,0.005485058,0.0009452463,0.0008440799,0.001437552,0.0001250619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008806174,0.00008310718,0.0133394,0.0001012379,0.00007301898,0.00006168602,0.000110818,0.9264666,0.001166929,0.006826217,0.0003711994,0.05131169],"study_design_scores_gemma":[0.00000505779,0.00002994349,0.002429414,0.00001886075,0.00001144814,0.000006626319,0.00002886569,0.991887,0.0005410359,0.004358635,0.0006705959,0.00001252287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2322574,0.0002340552,0.7588565,0.0002646741,0.00003678068,0.0003174716,0.002347899,0.0008357384,0.00484952],"genre_scores_gemma":[0.8513529,0.0001096027,0.1455546,0.00004542895,0.00002269879,0.000153457,0.001870327,0.0001000326,0.0007909237],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07295216,"threshold_uncertainty_score":0.1450551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008512209160577603,"score_gpt":0.2134483174250301,"score_spread":0.2049361082644525,"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."}}