{"id":"W2767745681","doi":"10.1016/j.energy.2017.10.135","title":"Corrigendum to “A sequential planning approach for Distributed Generation and natural gas networks” [Energy 127 (2017) 428–437]","year":2017,"lang":"en","type":"erratum","venue":"Energy","topic":"Integrated Energy Systems Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Natural gas; Energy (signal processing); Distributed generation; Computer science; Engineering; Waste management; Mathematics; Electrical engineering; Renewable energy; Statistics","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":[0.0001852775,0.0008004866,0.0007740296,0.0003242331,0.0004779093,0.0004790329,0.0005089159,0.001243511,0.0000131546],"category_scores_gemma":[0.00009088052,0.0008236317,0.0001739812,0.0001921093,0.0000540669,0.0002807957,0.0001058833,0.0005633686,0.000001993755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003994171,"about_ca_system_score_gemma":0.0001132351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000820305,"about_ca_topic_score_gemma":0.0005313959,"domain_scores_codex":[0.9973843,0.00008175492,0.0006084038,0.0007984251,0.000324516,0.0008025701],"domain_scores_gemma":[0.9984191,0.00002612446,0.0002858556,0.0007411945,0.0002815102,0.0002461994],"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":[0.00001588937,0.000006408032,8.005309e-7,0.00004529177,0.000133456,0.000008322381,0.00003085389,0.5154533,0.0001200088,0.001094305,0.482051,0.001040441],"study_design_scores_gemma":[0.0002306262,0.00003320858,0.000002684643,0.0001077918,0.00005780622,0.00001863216,0.00001301321,0.5889754,0.0002710653,0.00001188209,0.4097437,0.0005342392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.00001475375,0.004828117,0.9158976,0.0000255515,0.06825002,0.0002249427,0.0002616327,0.0004560964,0.01004127],"genre_scores_gemma":[0.183836,0.002025624,0.01756848,0.000366519,0.04853082,0.001904039,0.1961637,0.001194879,0.5484099],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8983291,"threshold_uncertainty_score":0.9994215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02426154547948646,"score_gpt":0.2310024540404853,"score_spread":0.2067409085609989,"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."}}