{"id":"W6957604113","doi":"10.6068/dp150396d39ce12","title":"Trend 1990 - 2008. United Nations Economic Commission for Europe. Macroeconomic Statistics [Archive]: GDP: Expenditure Approach, in National Currency | Country: Italy | Selection 1: GDP | Selection 2: Millions of NCUs at average prices of 2005, 1990-2008. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 054-001-004.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Linguistics and Language Analysis","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"National accounts; Commission; Currency; Official statistics; Economic statistics; Gross fixed capital formation; Measures of national income and output; International Standard Industrial Classification; Descriptive 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004732113,0.0004163844,0.0006905519,0.001171169,0.0002286606,0.0001692039,0.0008508114,0.0002345202,0.01152921],"category_scores_gemma":[0.00005774079,0.0003927021,0.000002782598,0.00009082897,0.0002326228,0.0002134657,0.0002466357,0.0003825986,0.0001728465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001257489,"about_ca_system_score_gemma":0.0003185896,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2922601,"about_ca_topic_score_gemma":0.3843566,"domain_scores_codex":[0.9974446,0.0001922011,0.0009425857,0.0007133347,0.0003664389,0.0003408732],"domain_scores_gemma":[0.9975529,0.0005066683,0.001020411,0.0006960856,0.00007904983,0.0001448411],"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":[0.00008746644,0.0002439034,0.000008839964,0.0003968868,0.0003262931,0.000005788516,0.00009408736,0.0003150411,0.000004824964,0.01944845,0.9790316,0.0000368238],"study_design_scores_gemma":[0.0006700101,0.00009347702,0.000002306265,0.00002884507,0.0003294309,0.00001667968,0.0001578929,0.03719589,1.190987e-7,0.000007957728,0.9611038,0.0003935935],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000001567827,0.0008444355,0.00006517443,0.000002375529,0.0003214561,0.0004946511,0.9653475,0.00003883738,0.032884],"genre_scores_gemma":[0.000156093,0.0009349845,0.0006751271,0.00001578174,0.000620109,0.00002683943,0.9862983,0.0001106465,0.01116216],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09209662,"threshold_uncertainty_score":0.9998525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03946188294069345,"score_gpt":0.2772207923424535,"score_spread":0.2377589094017601,"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."}}