{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003834298,0.0005423706,0.000656854,0.001355925,0.0003595538,0.0007813028,0.00101167,0.0003179476,0.0009201413],"category_scores_gemma":[0.00004386226,0.0005419571,0.0002275978,0.003426226,0.0002684834,0.009541974,0.000007524401,0.0007071393,0.00005143231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001950126,"about_ca_system_score_gemma":0.0002024494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003234717,"about_ca_topic_score_gemma":0.00825322,"domain_scores_codex":[0.995107,0.00003157844,0.00169327,0.001090417,0.001253143,0.0008245983],"domain_scores_gemma":[0.9974555,0.0003445623,0.0006235393,0.0006985977,0.000749099,0.0001287126],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002927507,0.001307839,0.9153676,0.00167961,0.0005058047,0.0001289488,0.004764083,0.002392679,0.0006280537,0.02915897,0.03975021,0.001388687],"study_design_scores_gemma":[0.003456689,0.0001025741,0.5648321,0.0001597251,0.0001108027,2.261254e-7,0.002272785,0.002775867,0.0006579806,0.006167845,0.4187952,0.0006681648],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7290754,0.00006552746,0.00163842,0.0003973359,0.0002324897,0.001374076,0.2668995,0.0002559873,0.00006125299],"genre_scores_gemma":[0.5323018,0.00001418199,0.0009711144,0.0001331346,0.0001867875,0.0001620783,0.466079,0.00008287391,0.00006902989],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.379045,"threshold_uncertainty_score":0.9999931,"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."}}