{"id":"W7037419690","doi":"","title":"Eficiencia del transporte férreo de carga Internacional:
\\nUn análisis a través de la envolvente de datos
\\n(Efficiency of railways international freight:
\\nan analysis with data envelopment)","year":2014,"lang":"en","type":"article","venue":"El Repositorio Academico Digital de la UANL (Universidad Autónoma de Nuevo León)","topic":"Pacific and Southeast Asian Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Data envelopment analysis; Benchmarking; Product (mathematics); Economic efficiency; Scale (ratio); Value (mathematics)","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":[0.002155648,0.0005811442,0.000380695,0.002202222,0.0002575351,0.002055831,0.0003752665,0.0005932559,0.004398933],"category_scores_gemma":[0.004113817,0.0001745971,0.0008082993,0.002530003,0.0003284844,0.0008114591,0.000505852,0.000516672,0.0008211057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002389716,"about_ca_system_score_gemma":0.0009646639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03197758,"about_ca_topic_score_gemma":0.02081516,"domain_scores_codex":[0.9990757,0.0002606228,0.00004902812,0.0001847895,0.000313934,0.0001159142],"domain_scores_gemma":[0.9969994,0.001630922,0.0003743322,0.0001614973,0.0007900723,0.00004374499],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004979834,0.0002612387,0.447445,0.0007710607,0.0004791018,0.0005760121,0.0008326666,0.4134026,0.005773293,0.01417297,0.004209454,0.1115786],"study_design_scores_gemma":[0.00003168144,0.0003782275,0.4402659,0.0004108462,0.0004861741,0.0004423998,0.002723467,0.5032973,0.02217892,0.006901347,0.02278289,0.0001008164],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9424407,0.0008492374,0.03042325,0.0005260493,0.00004246519,0.00005687533,0.002276207,0.0001904961,0.02319472],"genre_scores_gemma":[0.991028,0.0002834452,0.003420083,0.0000199081,0.00001004963,0.00002272772,0.0008587614,0.00002759938,0.004329489],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03197758,"threshold_uncertainty_score":0.0635829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009446214784056006,"score_gpt":0.2663359000911336,"score_spread":0.2568896853070776,"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."}}