{"id":"W3168225675","doi":"10.3886/e107861v2","title":"Budget deficits and money creation: Exploring their relation before Bretton Woods. Replication data.","year":2017,"lang":"en","type":"dataset","venue":"ICPSR Data Holdings","topic":"International Development and Aid","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Replication (statistics); Relation (database); Economics; Monetary economics; Computer science; Biology; Database","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"],"consensus_categories":[],"category_scores_codex":[0.002021636,0.0002785713,0.0002798552,0.0001869367,0.001272291,0.0008189974,0.003528867,0.000320745,0.0001281325],"category_scores_gemma":[0.002009634,0.0002749438,0.00002751079,0.0001436992,0.000253751,0.004167033,0.001737334,0.0003649156,0.0001382116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001411913,"about_ca_system_score_gemma":0.0002437007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003542587,"about_ca_topic_score_gemma":0.002214775,"domain_scores_codex":[0.997269,0.00008474118,0.0004084949,0.001218942,0.0006994424,0.0003193363],"domain_scores_gemma":[0.9948282,0.0001371424,0.0006421301,0.004099387,0.0001742928,0.0001188479],"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":[0.00001143618,0.00001930266,0.000542926,0.00003334024,0.00004441194,0.000003802927,0.0007444045,2.281657e-7,0.000005162489,0.002048943,0.9884512,0.008094825],"study_design_scores_gemma":[0.0001544975,0.00001417409,0.005297429,0.0002368005,0.00005955885,0.00000354221,0.0002195722,0.00006835068,0.00001266588,0.001236731,0.9923992,0.0002974857],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002733106,0.0003482456,0.00002673912,0.002670555,0.0009303039,0.0004290938,0.9909506,0.00008516018,0.001826174],"genre_scores_gemma":[0.003730443,0.003782765,0.0003840628,0.0001348351,0.001149902,0.00004588697,0.9898931,0.00002311912,0.0008558514],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.007797339,"threshold_uncertainty_score":0.9999703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.167660557444707,"score_gpt":0.3798803703557134,"score_spread":0.2122198129110064,"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."}}