{"id":"W3132920361","doi":"10.1049/iet-gtd.2020.0624","title":"Coordinated G&amp;TEP and carbon capture and storage expansion planning model for emission constrained power systems","year":2020,"lang":"en","type":"article","venue":"IET Generation Transmission & Distribution","topic":"Integrated Energy Systems Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec; Okanagan University College; University of British Columbia, Okanagan Campus; Kelowna General Hospital; University of British Columbia","funders":"","keywords":"Retrofitting; Renewable energy; Hydroelectricity; Electric power system; Carbon capture and storage (timeline); Greenhouse gas; Energy storage; Pumped-storage hydroelectricity; Integer programming; Electricity generation; Computer science; Environmental economics; Mathematical optimization; Environmental science; Engineering; Power (physics); Distributed generation; Climate change; Electrical engineering; Economics; Algorithm; Mathematics","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":[0.0001734671,0.0002946898,0.0002846151,0.00005613296,0.0002055298,0.0001210295,0.00005545331,0.000349368,0.000005827541],"category_scores_gemma":[0.00003972161,0.0002769238,0.00004352598,0.0001838312,0.0000333188,0.0002133196,0.000006173449,0.0001804007,4.98887e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009953538,"about_ca_system_score_gemma":0.00004394488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002112015,"about_ca_topic_score_gemma":0.00000178008,"domain_scores_codex":[0.9986933,0.00006551365,0.0004327097,0.0003747956,0.0001900293,0.0002436209],"domain_scores_gemma":[0.9993324,0.00002627209,0.00007914101,0.00011506,0.0001924696,0.0002546088],"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":[0.00005608341,0.000008287617,0.00001313428,0.0001374551,0.00001712256,0.000001230604,0.0009172219,0.6711884,0.3246671,0.0002025794,0.002442868,0.0003485536],"study_design_scores_gemma":[0.0008964405,0.00006672578,0.00001229882,0.0001482779,0.00004145207,0.00001225734,0.0002237815,0.9808689,0.01506364,0.00001075117,0.002354343,0.0003011303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1742793,0.001319843,0.822767,0.0002784915,0.0002096334,0.0005021254,0.0002257471,0.0003337957,0.00008410346],"genre_scores_gemma":[0.9948635,0.00008645263,0.001368918,0.00004137101,0.00007877978,0.00007242883,0.00333628,0.00004895212,0.0001033691],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.821398,"threshold_uncertainty_score":0.9999683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02149877728337733,"score_gpt":0.2220281210684175,"score_spread":0.2005293437850402,"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."}}