{"id":"W2026873606","doi":"10.3141/1825-01","title":"Pricing Commuter, Intercity, and Freight Trains in a Terminal Railway Context: An Approach","year":2003,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Operations research; Train; Context (archaeology); Transport engineering; Track (disk drive); Cost allocation; Computer science; Rail freight transport; Resource (disambiguation); Capital cost; Economics; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001708058,0.001385562,0.001213048,0.001889984,0.001105168,0.005233566,0.002974091,0.002569361,0.005668642],"category_scores_gemma":[0.003577109,0.001193639,0.001698442,0.002577444,0.002260061,0.005239547,0.00261749,0.00287163,0.0004587659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007290651,"about_ca_system_score_gemma":0.004408007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02608279,"about_ca_topic_score_gemma":0.01720945,"domain_scores_codex":[0.998578,0.0006886941,0.00004108823,0.0001916042,0.0003397572,0.0001609022],"domain_scores_gemma":[0.9989557,0.0004971989,0.00008357195,0.00008474888,0.0002674478,0.0001112871],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001559316,0.00008397891,0.0004472479,0.00005361862,0.00003796962,0.00016818,0.0001120946,0.331673,0.000245036,0.6533341,0.002713062,0.01111613],"study_design_scores_gemma":[0.00001234629,0.00003502551,0.0003785722,0.0000255463,0.00002809547,0.00005116723,0.0001439643,0.6662084,0.00008674902,0.3251566,0.007850396,0.00002317519],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02139114,0.001485134,0.9398432,0.003269644,0.0002434593,0.0001334517,0.0002482671,0.00006850067,0.03331728],"genre_scores_gemma":[0.6796961,0.005037845,0.2753904,0.0008942712,0.001175929,0.0006258074,0.0003196848,0.0002301001,0.03662983],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02608279,"threshold_uncertainty_score":0.05289757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1193850557478279,"score_gpt":0.4021458802293988,"score_spread":0.2827608244815709,"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."}}