{"id":"W4409349342","doi":"10.1038/s41598-025-97088-y","title":"Bio-inspired computational intelligence metaheuristic-based optimization and sensitivity analysis approach to determine techno-economic feasibility of hydrogen refueling stations for fuel cell vehicles","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Hybrid Renewable Energy Systems","field":"Energy","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"King Saud University","keywords":"Sensitivity (control systems); Fuel cells; Metaheuristic; Computer science; Operations research; Artificial intelligence; Chemical engineering; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.002191103,0.0001690726,0.0004203247,0.0008288194,0.0002418297,0.0001325326,0.00008771225,0.00007077119,0.00000579747],"category_scores_gemma":[0.0003307177,0.0001736029,0.0001592302,0.001357087,0.0001951493,0.0001121914,0.00006310755,0.00004323011,7.987904e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001654158,"about_ca_system_score_gemma":0.0003058887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001221212,"about_ca_topic_score_gemma":0.0006973531,"domain_scores_codex":[0.9975681,0.0001243396,0.0009196605,0.0009275386,0.0002437372,0.000216681],"domain_scores_gemma":[0.998113,0.0002560825,0.0004523408,0.0006950494,0.0003860191,0.00009745929],"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":[0.00001403847,0.0001029871,0.003356757,0.0002080436,0.0001114526,0.000005443212,0.00006212966,0.9914495,0.003942423,0.0001811141,0.00002832767,0.0005377277],"study_design_scores_gemma":[0.0001071825,0.00001345071,0.0002479635,0.00001886642,0.0002599632,0.000006146554,0.00006916322,0.940228,0.0564426,0.002240752,0.0002209644,0.00014498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3593697,0.00007645593,0.639196,0.0000276122,0.0003595896,0.0004210788,0.00004111517,0.00005812267,0.0004503411],"genre_scores_gemma":[0.9130199,0.000001023815,0.08612213,0.00001151908,0.0000127846,0.00006280569,0.0004436173,0.00001259136,0.0003136563],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5536502,"threshold_uncertainty_score":0.7079321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02632093939515532,"score_gpt":0.2697654389786218,"score_spread":0.2434444995834665,"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."}}