{"id":"W2981000588","doi":"10.1111/jiec.12954","title":"Life cycle assessment of emerging technologies: Evaluation techniques at different stages of market and technical maturity","year":2019,"lang":"en","type":"article","venue":"Journal of Industrial Ecology","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":210,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Calgary","funders":"University of Calgary; Engineering and Physical Sciences Research Council; U.S. Department of Energy","keywords":"Emerging technologies; Maturity (psychological); Context (archaeology); Life-cycle assessment; Computer science; Emerging markets; Technology readiness level; Set (abstract data type); Industrial ecology; Data science; Risk analysis (engineering); Process management; Management science; Business; Systems engineering; Engineering; Production (economics); Sustainability; Economics","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001702436,0.000123386,0.0004423113,0.00009893769,0.00003868506,0.000005682637,0.0002173394,0.0003168452,0.002490465],"category_scores_gemma":[0.0008406982,0.00009411074,0.00008769289,0.0001129897,0.000369268,0.0001669305,0.0004424945,0.0003907634,0.000001046047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008350126,"about_ca_system_score_gemma":0.00006281742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002319507,"about_ca_topic_score_gemma":0.00002493881,"domain_scores_codex":[0.9983043,0.0002458157,0.0006702112,0.0001548529,0.0004362378,0.00018855],"domain_scores_gemma":[0.9987282,0.0002016869,0.0007550113,0.0002037463,0.00002805283,0.00008332994],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001554312,0.0002629642,0.9599248,0.00001825414,0.00002726222,0.000002099873,0.00003656535,0.0001649417,0.02834015,0.00001898127,0.0008999174,0.01014868],"study_design_scores_gemma":[0.001170781,0.001455066,0.9773173,0.00003344765,0.00006643248,0.00002966574,0.0006682138,0.0003868204,0.01748287,0.0009908964,0.0002868738,0.0001116354],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969888,0.0000368994,0.00001906011,0.0007690416,0.0001246919,0.0004837972,0.000006238417,0.00001173046,0.001559688],"genre_scores_gemma":[0.9990686,0.0000988688,0.0007333886,0.00002212528,0.00002047346,0.000006548106,9.940986e-7,0.000006491625,0.00004251269],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01739255,"threshold_uncertainty_score":0.9984214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01793006089169132,"score_gpt":0.3073883513702872,"score_spread":0.2894582904785959,"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."}}