{"id":"W7055957028","doi":"","title":"1997 Economic Census: Transportation: 1997 Commodity Flow Survey: Oklahoma City, OK MSA","year":2000,"lang":"en","type":"other","venue":"Rosa P: A digital library for transportation research (United States Department of Transportation)","topic":"Thermal properties of materials","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Metropolitan area; Census; Commodity; Population; General partnership; Survey data collection; Quarter (Canadian coin); Principal (computer security); State (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":[0.00075272,0.001054848,0.001457394,0.0009283694,0.0003650801,0.0006654934,0.001348633,0.0006492015,0.01344698],"category_scores_gemma":[0.0000196915,0.001078555,0.0005917173,0.0007519377,0.0007298867,0.002342379,0.00001097138,0.0004838896,0.0004833866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001044608,"about_ca_system_score_gemma":0.0004606351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001591358,"about_ca_topic_score_gemma":0.002211213,"domain_scores_codex":[0.9931817,0.0003864842,0.002172912,0.001433595,0.001485736,0.001339568],"domain_scores_gemma":[0.9966917,0.0006588423,0.0009135552,0.0009093994,0.0002470339,0.0005794399],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003277333,0.0008026761,0.0008503948,0.00163023,0.0003790942,0.00007496228,0.0006201905,0.0008533481,0.00004018569,0.0004489998,0.9902884,0.0007342216],"study_design_scores_gemma":[0.002373598,0.0006311366,0.002321211,0.0004920725,0.0001668504,0.000001026786,0.0002277376,0.0001347742,0.006626991,0.0004938093,0.9854391,0.001091699],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.2139645,0.0003894129,0.0002657564,0.0003266805,0.0004343619,0.002554811,0.7811105,0.0006711784,0.0002828316],"genre_scores_gemma":[0.07500041,0.0006804958,0.001673904,0.0001147712,0.0002196851,0.0007348674,0.9075462,0.0008797888,0.01314983],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.138964,"threshold_uncertainty_score":0.9991665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04791649066043588,"score_gpt":0.2861874279018381,"score_spread":0.2382709372414022,"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."}}