{"id":"W2759234195","doi":"10.1007/s11367-017-1400-1","title":"Assessing the individual and combined effects of uncertainty and variability sources in comparative LCA of pavements","year":2017,"lang":"en","type":"article","venue":"The International Journal of Life Cycle Assessment","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":60,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Sciences and Engineering Research Council of Canada; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Monte Carlo method; Uncertainty analysis; Life-cycle assessment; Computer science; Asphalt; Probability distribution; Midpoint; Scenario analysis; Environmental science; Statistics; Production (economics); Mathematics; Simulation","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":[],"consensus_categories":[],"category_scores_codex":[0.001922723,0.00008924781,0.0002172367,0.00002481774,0.0001339631,0.0001092403,0.0005906646,0.00002491798,0.00003973574],"category_scores_gemma":[0.0002562574,0.00005084139,0.00004305047,0.00002586192,0.0008850697,0.0004235517,0.0005160926,0.000168356,1.83255e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001574809,"about_ca_system_score_gemma":0.00003830408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004455482,"about_ca_topic_score_gemma":0.0000451868,"domain_scores_codex":[0.9985393,0.0002603926,0.000409329,0.00009725366,0.0005944404,0.00009928823],"domain_scores_gemma":[0.9984241,0.0005630073,0.0007430047,0.0001880451,0.00002799779,0.00005390431],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005991463,0.0002753215,0.9913123,0.00001602822,0.0001081451,0.00000342393,0.002054828,0.001655518,0.00194468,0.0002103728,0.00001424522,0.002345218],"study_design_scores_gemma":[0.0009564747,0.0001461969,0.9891986,0.00004681887,0.00002758523,0.000004482024,0.001958837,0.001769352,0.001033093,0.004795125,0.00001662699,0.00004677699],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976712,0.00002948078,0.0001049055,0.001369841,0.0001026929,0.0001767158,0.000004062399,8.564611e-7,0.0005401794],"genre_scores_gemma":[0.9996132,0.00002929737,0.0002383759,0.00008795475,0.00001905589,0.000002797764,6.759972e-7,0.000002818404,0.000005862928],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004584752,"threshold_uncertainty_score":0.3261076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02512602167572071,"score_gpt":0.350693233149212,"score_spread":0.3255672114734913,"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."}}