{"id":"W4413097117","doi":"10.1016/j.jrst.2025.07.004","title":"Optimizing mountain railway alignments with a potential field guided 3D-RRT-star algorithm","year":2025,"lang":"en","type":"article","venue":"Journal of Railway Science and Technology","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Fundamental Research Funds for Central Universities of the Central South University; China Scholarship Council; National Natural Science Foundation of China; Nanyang Technological University","keywords":"Algorithm; Field (mathematics); Star (game theory); Computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005611687,0.0007153746,0.000816474,0.0008084722,0.0004594883,0.0005806413,0.001122952,0.001186305,0.003193761],"category_scores_gemma":[0.001192774,0.0005022106,0.0008726069,0.0007395648,0.0004758262,0.0006624256,0.0009877143,0.0006710408,0.000568622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005405886,"about_ca_system_score_gemma":0.001699632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008179074,"about_ca_topic_score_gemma":0.00974632,"domain_scores_codex":[0.9997645,0.0000554721,0.00001206769,0.00005568624,0.00007500081,0.00003729456],"domain_scores_gemma":[0.9996351,0.0001794645,0.00003958441,0.00002370395,0.00009154411,0.00003058934],"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":[0.00005021315,0.00003690718,0.0006158687,0.00005453695,0.00002578685,0.00008601422,0.00006094739,0.9429903,0.001826103,0.003651187,0.001738237,0.04886391],"study_design_scores_gemma":[0.000009570496,0.0000168721,0.00005668626,0.000003421522,0.000003565465,0.00001831925,0.00001024106,0.9983575,0.0002634722,0.000850865,0.0004063204,0.000003091656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0265296,0.0001565693,0.9686086,0.0001290487,0.00002861984,0.00006981443,0.00008537365,0.0007312503,0.003661168],"genre_scores_gemma":[0.315034,0.000120611,0.6799236,0.0001226272,0.0000193101,0.0002574739,0.0003781327,0.0002449272,0.003899452],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008179074,"threshold_uncertainty_score":0.01626295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007573736935442239,"score_gpt":0.2530745373715286,"score_spread":0.2455008004360863,"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."}}