{"id":"W2358156957","doi":"","title":"Optimize the Occupation Plan of Arrival-departure Lines in Passenger Station based on Genetic Algorithm","year":2007,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"CAE (Canada)","funders":"","keywords":"Plan (archaeology); Crossover; Genetic algorithm; Fitness function; Operations research; Key (lock); Function (biology); Mutation; Arrival time; Integer programming; Chromosome; Engineering; Computer science; Algorithm; Transport engineering; Artificial intelligence; Geography; Machine learning; Archaeology; Computer security","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":[0.000278581,0.0005291054,0.0006421951,0.0005228113,0.000368991,0.0005419029,0.0005648747,0.0006459358,0.001911959],"category_scores_gemma":[0.0005705745,0.000284291,0.0004259942,0.0007576943,0.0003196129,0.0004605881,0.0002584089,0.0003447133,0.0001493212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001000489,"about_ca_system_score_gemma":0.001758745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02715173,"about_ca_topic_score_gemma":0.01775692,"domain_scores_codex":[0.9998642,0.00002923109,0.000004344234,0.0000293721,0.00003046227,0.00004231579],"domain_scores_gemma":[0.9999001,0.00004502417,0.00001412531,0.000005826002,0.000022511,0.00001227926],"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":[0.00002070341,0.00002212058,0.0005047353,0.00001687757,0.000008301588,0.00002260304,0.00002833083,0.9853941,0.0008746632,0.002158186,0.000289598,0.01065985],"study_design_scores_gemma":[0.00001121761,0.00002073504,0.0002037072,0.00000226191,0.000005859239,0.000006767671,0.00001691746,0.998266,0.0002718049,0.0009493999,0.000242314,0.000002922825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2267592,0.0001913454,0.7620638,0.0002072883,0.00002663578,0.0001135373,0.0001136134,0.0004004391,0.01012406],"genre_scores_gemma":[0.8455265,0.0001795303,0.1492063,0.00004342985,0.00001004667,0.0001261914,0.0001744088,0.00004623532,0.004687293],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02715173,"threshold_uncertainty_score":0.05398738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008161835085287335,"score_gpt":0.2146234231928678,"score_spread":0.2064615881075805,"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."}}