{"id":"W2572606024","doi":"10.1115/1.4035729","title":"Improving the Resilience of Energy Flow Exchanges in Eco-Industrial Parks: Optimization Under Uncertainty","year":2017,"lang":"en","type":"article","venue":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B Mechanical Engineering","topic":"Sustainable Industrial Ecology","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Industrial symbiosis; Resilience (materials science); Environmental economics; Process (computing); Computer science; Risk analysis (engineering); Robust optimization; Production (economics); Optimal design; Industrial engineering; Operations research; Engineering; Mathematical optimization; Business; Economics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002275956,0.0004095076,0.0009180752,0.0005053575,0.0001141347,0.000140041,0.0006799549,0.0004789541,0.000008375142],"category_scores_gemma":[0.002353483,0.0003490193,0.0001419666,0.0003513003,0.00005764514,0.0004187642,0.0001325369,0.001093111,3.205491e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005491675,"about_ca_system_score_gemma":0.0001273817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008958067,"about_ca_topic_score_gemma":0.0002212853,"domain_scores_codex":[0.9971387,0.0001303315,0.001381221,0.0002854597,0.0003865157,0.0006777951],"domain_scores_gemma":[0.9976336,0.0009445507,0.0005540851,0.0005229051,0.0001559936,0.00018886],"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":[0.0000605051,0.00002325867,0.0002200397,0.0001469409,0.00007318147,0.00004715598,0.0001483186,0.9943917,0.0004324484,0.0008905299,0.00003275695,0.003533205],"study_design_scores_gemma":[0.00170205,0.0001345984,0.0002974473,0.0006988833,0.00005355091,0.0000762815,0.0005229748,0.9952269,0.0002578013,0.00003474062,0.0006608446,0.0003339582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6551776,0.004104649,0.3315762,0.0002015279,0.007958322,0.0007324567,0.00004260759,0.0001631765,0.00004348591],"genre_scores_gemma":[0.9975104,0.0009715112,0.0007196349,0.00000433887,0.0006795381,0.00003770674,0.000002877379,0.0000651025,0.000008936502],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3423328,"threshold_uncertainty_score":0.9998962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01275065265589235,"score_gpt":0.2122281306848871,"score_spread":0.1994774780289948,"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."}}