{"id":"W4200247305","doi":"10.1016/j.apenergy.2021.118299","title":"Development of an integrated bi-level model for China’s multi-regional energy system planning under uncertainty","year":2021,"lang":"en","type":"article","venue":"Applied Energy","topic":"Integrated Energy Systems Optimization","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Renewable energy; China; Energy supply; Probabilistic logic; Energy planning; Electricity; Climate change; Environmental economics; Time horizon; Fossil fuel; Environmental science; Natural resource economics; Business; Energy (signal processing); Computer science; Engineering; Economics; Geography; Mathematics; Waste management","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":[0.0001330478,0.0003636907,0.0004275746,0.0001878586,0.0001239399,0.00003549251,0.0002212373,0.0002876854,0.00000743257],"category_scores_gemma":[0.000006101544,0.0003597191,0.00008020373,0.0003609238,0.00002936344,0.000103448,0.00003419495,0.0001060946,0.000001253639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004280749,"about_ca_system_score_gemma":0.0003204051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003913567,"about_ca_topic_score_gemma":0.001317859,"domain_scores_codex":[0.9982772,0.00002872942,0.0006573329,0.0003958394,0.0002450869,0.0003958477],"domain_scores_gemma":[0.9991463,0.00003271745,0.0001196547,0.0003329621,0.0002449362,0.0001234741],"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":[0.00003155917,0.00005413476,4.356129e-7,0.00008708563,0.0001617076,0.000002586223,0.0005366244,0.8717678,0.02263794,0.1007972,0.0001321039,0.003790749],"study_design_scores_gemma":[0.0007602005,0.000009409773,0.0000128137,0.0001325488,0.00002194416,0.00001098757,0.001262494,0.9479114,0.04662802,0.00008640919,0.002784766,0.0003789559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0146616,0.0001224477,0.9829904,0.000006082882,0.0002509818,0.00007541316,0.00004131173,0.0004136056,0.001438084],"genre_scores_gemma":[0.8130334,0.000006580623,0.1845956,0.00004449861,0.00006125377,0.0002618794,0.001395577,0.0001034003,0.0004977694],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7983949,"threshold_uncertainty_score":0.9998855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03668529506183706,"score_gpt":0.2338523893618531,"score_spread":0.1971670943000161,"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."}}