{"id":"W6901562534","doi":"10.6068/dp1712d9c976072","title":"RANKING: United States Census Bureau. International Trade Datasets: Exports - Agricultural and Non-Agricultural Products | Indicator: Total Value, 2018. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 001-067-002","year":2020,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Commodity; Exportation; Agriculture; Official statistics; Balance of trade; Publishing; Export trade; Trade barrier","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":[0.001685099,0.002047234,0.001889156,0.006299178,0.0008817973,0.002896196,0.003017193,0.001206697,0.09235318],"category_scores_gemma":[0.01377982,0.0008698484,0.001273573,0.02124928,0.0004295099,0.002911914,0.001885628,0.003132451,0.1240994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002445045,"about_ca_system_score_gemma":0.00491396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07527988,"about_ca_topic_score_gemma":0.05866171,"domain_scores_codex":[0.9977618,0.0002702538,0.0004231567,0.0005288619,0.000724404,0.0002914907],"domain_scores_gemma":[0.9895784,0.00129545,0.001004097,0.001005293,0.006598013,0.0005186569],"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":[0.00001318497,0.000008442145,0.0004695338,0.0001904419,0.00001095537,0.000004640199,0.00000853683,0.00006049695,0.00001287321,0.0002941611,0.9976109,0.001315797],"study_design_scores_gemma":[0.000136514,0.00001231205,0.007267928,0.0004539348,0.00002905996,0.00002104794,0.0001283393,0.0002376842,0.0001040344,0.001133227,0.990444,0.00003200628],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004041505,0.00003468166,0.00003944564,0.00006574527,0.00003180738,0.00001377435,0.9991201,0.00006615515,0.0005879528],"genre_scores_gemma":[0.0002828464,0.000112785,0.0002133581,0.00007693076,0.00002256234,0.0001287457,0.9981726,0.00007033805,0.0009198257],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09235318,"threshold_uncertainty_score":0.3089522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02892725523627964,"score_gpt":0.2632817196985075,"score_spread":0.2343544644622279,"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."}}