{"id":"W2086183677","doi":"10.1016/j.trc.2015.03.026","title":"Modeling dry-port-based freight distribution planning","year":2015,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Maritime Ports and Logistics","field":"Engineering","cited_by":100,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"Canada Research Chairs","keywords":"Port (circuit theory); Integer programming; Computer science; Service (business); Operations research; Network planning and design; Integer (computer science); Bulk cargo; Transport engineering; Mathematical optimization; Engineering; Marine engineering; Computer network; Mathematics; Business","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007690734,0.0001785583,0.000192312,0.0002728742,0.0001782681,0.00005672452,0.0002864937,0.0002039903,0.00003951594],"category_scores_gemma":[0.000203763,0.0001776727,0.00004976935,0.0005964297,0.0001574981,0.0001610906,0.00001199317,0.0005848949,0.00002142239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001072481,"about_ca_system_score_gemma":0.00007192049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001573666,"about_ca_topic_score_gemma":0.000074802,"domain_scores_codex":[0.9981307,0.00002663875,0.0003907447,0.0002640729,0.0006168241,0.0005710108],"domain_scores_gemma":[0.9991699,0.00005782696,0.00002686848,0.0003485066,0.0002985505,0.00009838903],"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":[0.00001696359,0.0000283085,0.00744764,0.0001002359,0.00002252592,0.00007879455,0.0001939436,0.9713812,0.00007429114,0.004919604,0.01102867,0.004707772],"study_design_scores_gemma":[0.0003724663,0.00005860688,0.0005160771,0.0001181984,0.0000149898,4.369277e-7,0.001874032,0.9451836,0.002750094,0.005467013,0.04334161,0.000302877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2453973,0.001503252,0.7407365,0.001235069,0.0004229732,0.0004927315,0.0002162803,0.00742372,0.002572234],"genre_scores_gemma":[0.9959948,0.00008643404,0.002889126,0.000005657114,0.0000541095,0.0001118609,0.0007714228,0.00003381602,0.00005275723],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7505975,"threshold_uncertainty_score":0.7245284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1299009964121976,"score_gpt":0.3450167711140081,"score_spread":0.2151157747018106,"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."}}