{"id":"W7114989590","doi":"10.1155/er/6686996","title":"Optimizing Transportation and Storage Design for CO <sub>2</sub> Geological Sequestration Using Multiobjective Optimization and Nodal Analysis: A Case Study From the Gunsan Basin, South Korea","year":2025,"lang":"en","type":"article","venue":"International Journal of Energy Research","topic":"CO2 Sequestration and Geologic Interactions","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Virtual Materials Group (Canada)","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; Ministry of Trade, Industry and Energy; Korea Institute of Geoscience and Mineral Resources; Korea Institute of Energy Technology Evaluation and Planning; Ministry of Science, ICT and Future Planning; National Research Foundation","keywords":"Subsea; Inflow; Multi-objective optimization; Pipeline (software); Nodal analysis; Scope (computer science); Storage tank; Fossil fuel","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005029799,0.0009389964,0.000467277,0.0005704193,0.0003880369,0.0008808244,0.0003889739,0.0007142928,0.001121736],"category_scores_gemma":[0.0006077222,0.0003923904,0.0005794717,0.0004768635,0.0004351219,0.0005122296,0.0005548037,0.0003375098,0.000100798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009062639,"about_ca_system_score_gemma":0.00129792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01012254,"about_ca_topic_score_gemma":0.01423382,"domain_scores_codex":[0.9998515,0.00005916504,0.000005469093,0.00002422124,0.00002929167,0.00003026815],"domain_scores_gemma":[0.9998043,0.0001193435,0.00002075606,0.000008321945,0.0000342661,0.00001302585],"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":[0.00001804903,0.00001871383,0.0006406576,0.0000299688,0.00001113783,0.00006975795,0.00002079215,0.9917281,0.002363313,0.0005284876,0.00005187031,0.004519108],"study_design_scores_gemma":[0.000006353597,0.00006011367,0.0004567341,0.000004147713,0.00001042571,0.0000136167,0.00006859876,0.9974745,0.001264577,0.000381376,0.0002547412,0.000004877189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.768338,0.0002627739,0.2196665,0.0002257121,0.00001558242,0.0001811228,0.000157676,0.0001364361,0.01101622],"genre_scores_gemma":[0.9667549,0.00009488039,0.03160679,0.00001619149,0.000001651689,0.00008350208,0.00005274472,0.00002927109,0.001360272],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01012254,"threshold_uncertainty_score":0.02012724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07120817039528572,"score_gpt":0.3734062484270578,"score_spread":0.3021980780317721,"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."}}