{"id":"W2802985882","doi":"10.1021/acs.est.7b04517","title":"Novel Method of Sensitivity Analysis Improves the Prioritization of Research in Anticipatory Life Cycle Assessment of Emerging Technologies","year":2018,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Future Earth","funders":"Division of Engineering Education and Centers; Division of Electrical, Communications and Cyber Systems; United States Agency for International Development; U.S. Environmental Protection Agency","keywords":"Commercialization; Life-cycle assessment; Sensitivity (control systems); Risk analysis (engineering); Computer science; Reduction (mathematics); Emerging technologies; Biochemical engineering; Prioritization; Uncertainty analysis; Reliability engineering; Environmental science; Environmental economics; Operations research; Management science; Engineering; Production (economics); Mathematics; Business; Artificial intelligence; Economics; Simulation","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.004922259,0.0001573709,0.0003848751,0.0009728466,0.0002584218,0.000008168546,0.000767422,0.0001659645,0.0001087406],"category_scores_gemma":[0.0003336073,0.0001247223,0.00008576436,0.005774681,0.0173418,0.0003289794,0.001599025,0.0003301515,0.000003888039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006556888,"about_ca_system_score_gemma":0.00005722681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007249041,"about_ca_topic_score_gemma":0.0001680607,"domain_scores_codex":[0.9971735,0.0001824327,0.0005527668,0.0005681934,0.0009708743,0.0005522468],"domain_scores_gemma":[0.9985948,0.0001688016,0.0003134802,0.0008529035,0.00001605967,0.00005393528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000006368816,0.0002695996,0.3997851,0.000007669856,0.00001405325,7.898464e-7,0.000300136,0.001711448,0.589767,0.0003180135,5.928751e-7,0.007819201],"study_design_scores_gemma":[0.0001483432,0.0002217455,0.7030469,0.000009227073,0.00003042496,0.000003351476,0.007364005,0.0158469,0.2723248,0.0008907994,0.00001876648,0.00009465615],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9872933,0.00003624882,0.01108108,0.0004134282,0.00003005316,0.0004103454,0.0000128285,0.00003206224,0.0006906812],"genre_scores_gemma":[0.9871079,0.00003693078,0.01279887,0.00001101536,0.000004455755,0.00001844421,0.000001352724,0.000008093849,0.00001289676],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3174422,"threshold_uncertainty_score":0.9853324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02192947539332627,"score_gpt":0.3810471709528762,"score_spread":0.3591176955595499,"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."}}