{"id":"W2572186048","doi":"10.1155/2017/6173795","title":"Energy-Efficient Train Operation Using Nature-Inspired Algorithms","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Engineering and Dynamics","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Eskişehir Osmangazi Üniversitesi","keywords":"Punctuality; Evolutionary algorithm; Computer science; Fitness function; Simulated annealing; Algorithm; Nonlinear system; Train; Process (computing); Convergence (economics); Mathematical optimization; Firefly algorithm; Genetic algorithm; Firefly protocol; Penalty method; Track (disk drive); Engineering; Artificial intelligence; Machine learning; Mathematics; Particle swarm optimization","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001081801,0.0001173545,0.000166746,0.0001008752,0.0001164853,0.00005177655,0.0001367989,0.000100635,0.00000328056],"category_scores_gemma":[0.00001184159,0.0001147338,0.00008633353,0.00004423964,0.00001638386,0.0003756616,8.236715e-7,0.0002223375,3.954516e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007342036,"about_ca_system_score_gemma":0.0000221419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000361075,"about_ca_topic_score_gemma":0.00001598956,"domain_scores_codex":[0.9992517,0.000005028402,0.0003417896,0.0000745509,0.0001975469,0.0001293676],"domain_scores_gemma":[0.9995229,0.00001098706,0.0001602678,0.0001432287,0.00009719237,0.0000654786],"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":[0.00001371151,0.00001316428,0.00006624974,0.00001868534,0.00002316767,0.00002115001,0.0002639264,0.9581823,0.02794708,0.0002501805,0.000003571466,0.01319683],"study_design_scores_gemma":[0.0008192508,0.00005216046,0.0351678,0.00009643035,0.00004096412,0.00001924918,0.00008785885,0.9552888,0.007671657,0.0001252512,0.0004539363,0.0001766983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6232672,0.000269293,0.3751738,0.00001927163,0.001126521,0.00002962772,0.00001141027,0.00003605647,0.00006676889],"genre_scores_gemma":[0.9701306,0.00009457031,0.02952878,0.000008489076,0.0001841158,0.000001172352,0.00001511352,0.00002704502,0.00001009493],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3468634,"threshold_uncertainty_score":0.467871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007304261957306004,"score_gpt":0.239479320464978,"score_spread":0.232175058507672,"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."}}