{"id":"W2062132757","doi":"10.21949/1501510","title":"Freight Transportation in South Dakota: Selected Data from Federal Sources","year":2010,"lang":"en","type":"article","venue":"Rosa P: A digital library for transportation research (United States Department of Transportation)","topic":"Transportation Systems and Infrastructure","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Directory; Truck; Government (linguistics); Commodity; Transport engineering; Business; Table (database); Database; Finance; Engineering; Computer science","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":[0.0008344686,0.0004469088,0.0004248451,0.01721257,0.001119432,0.001922575,0.0005357935,0.0003008059,0.0113689],"category_scores_gemma":[0.003166662,0.000380143,0.0003111161,0.04857792,0.0001937179,0.0009441399,0.001358444,0.0004042244,0.00453431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003532414,"about_ca_system_score_gemma":0.005292998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.459304,"about_ca_topic_score_gemma":0.5647002,"domain_scores_codex":[0.9990003,0.00009181575,0.0001665122,0.0001488375,0.0004353984,0.0001572499],"domain_scores_gemma":[0.9948397,0.0007737683,0.0009385891,0.0004810957,0.002728496,0.0002383857],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003220871,0.0002032188,0.4631671,0.003328918,0.0004151578,0.0003686073,0.003204912,0.001259985,0.001702114,0.001736878,0.3958212,0.1284699],"study_design_scores_gemma":[0.00002124854,0.0000128777,0.844798,0.0005422947,0.00008458055,0.00008787767,0.002356921,0.0002992017,0.0007472367,0.0001209757,0.1508948,0.00003388739],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.09792677,0.00224295,0.000332125,0.0005861698,0.00003956493,0.0002139088,0.8758198,0.0001827014,0.02265609],"genre_scores_gemma":[0.1181367,0.005641579,0.001378865,0.0001343635,0.0000303384,0.0004979614,0.8653265,0.00009747736,0.008756254],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.459304,"threshold_uncertainty_score":0.9132611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03343581761721229,"score_gpt":0.2630888359720064,"score_spread":0.2296530183547942,"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."}}