{"id":"W6894327380","doi":"10.5683/sp2/rklj9g","title":"Revenue, Expenditure, Assets, and Liabilities (REAL) public master file","year":2020,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Revenue; Government (linguistics); Public finance; Per capita; Population; Government revenue; National accounts; Economic statistics","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":[0.0009404397,0.001027723,0.001006249,0.003942563,0.0007932857,0.002353098,0.001799593,0.0008089812,0.07401079],"category_scores_gemma":[0.005951379,0.0007169283,0.0006248046,0.009684141,0.0002884914,0.001472269,0.001231678,0.001665627,0.08947047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004245244,"about_ca_system_score_gemma":0.007994092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2010935,"about_ca_topic_score_gemma":0.2097188,"domain_scores_codex":[0.9987354,0.00006957045,0.0001303302,0.0002676925,0.0005372674,0.000259752],"domain_scores_gemma":[0.995729,0.0004581728,0.0004871456,0.000647758,0.002303545,0.0003743683],"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":[0.00001837793,0.000007897843,0.000903283,0.0001094145,0.000007257269,0.000007092968,0.00001235294,0.00008701968,0.00003410152,0.0006173195,0.9968755,0.00132033],"study_design_scores_gemma":[0.00004615521,0.000005866801,0.008403838,0.0001245773,0.000009799757,0.00002237421,0.00007078539,0.0001670526,0.0002422402,0.0004273335,0.9904621,0.00001781357],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006115356,0.000007722352,0.00002239452,0.00002490726,0.000006635856,0.000006031756,0.9989109,0.00006851408,0.0008917963],"genre_scores_gemma":[0.0003605709,0.00002522172,0.0001374876,0.00002221472,0.000005415529,0.00004759831,0.9980295,0.00003981227,0.001332095],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2010935,"threshold_uncertainty_score":0.399846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04095943856380363,"score_gpt":0.2639164481088326,"score_spread":0.222957009545029,"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."}}