{"id":"W6957734193","doi":"10.6068/dp155721a4e5852","title":"TREND: International Monetary Fund. Direction of Trade Statistics: Imports | Country: United Kingdom | Trading Partner: HUNGARY, 1980/1 - 2015/4. Data-Planet™ Statistical Datasets by Conquest Systems, Inc. Dataset-ID: 056-002-004","year":2016,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mandate; CONQUEST; Payment; Value (mathematics); Exchange rate; Balance of payments; Quarter (Canadian coin); Position (finance)","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":[0.00142036,0.002787167,0.002414342,0.007471368,0.0008138345,0.005287051,0.002766262,0.00173098,0.1507682],"category_scores_gemma":[0.01078667,0.001433839,0.00117805,0.01893894,0.0004649196,0.004903651,0.00194197,0.003295785,0.3035897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002868926,"about_ca_system_score_gemma":0.004062034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05732794,"about_ca_topic_score_gemma":0.03101448,"domain_scores_codex":[0.9976811,0.0001766496,0.0004100013,0.0005199732,0.0008972337,0.0003150215],"domain_scores_gemma":[0.9911533,0.000765799,0.001544354,0.0007863041,0.005321133,0.0004291693],"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":[0.00002345499,0.000005088715,0.0003585053,0.0001716296,0.000009607961,0.000006287272,0.000007289901,0.00005765008,0.00001363664,0.0002952476,0.9975796,0.001471951],"study_design_scores_gemma":[0.00006537994,0.00001284837,0.006260036,0.0002340776,0.00001610638,0.00001612612,0.00008135215,0.00009707159,0.0001142536,0.0004765335,0.9926041,0.00002215855],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006152311,0.00006134412,0.00003912153,0.0001031113,0.00008688535,0.00001399021,0.9976545,0.0001270808,0.001852462],"genre_scores_gemma":[0.0003901649,0.0001583937,0.0001378157,0.00004920091,0.00004659399,0.000106326,0.9955518,0.0001382425,0.003421507],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1507682,"threshold_uncertainty_score":0.5043698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04158191350194012,"score_gpt":0.3147370621401931,"score_spread":0.273155148638253,"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."}}