{"id":"W6958434715","doi":"10.6068/dp152bf3ac70439","title":"Trend 1990 - 2006. United Nations Economic Commission for Europe. Gender Statistics [Archive]: Unemployment by Age | Country: Russia | Selection 1: Total (15+) | Selection 2: Both sexes | Selection 3: Unemployed, 1990-2006. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 054-003-027.","year":2016,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Commission; Unemployment; Commonwealth; Official statistics; Selection (genetic algorithm); Economic statistics; Per capita; International Standard Industrial Classification","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":[0.00184898,0.001737469,0.001861984,0.004538574,0.0008449886,0.002770958,0.002153933,0.001068799,0.08241418],"category_scores_gemma":[0.01250439,0.001107248,0.001052922,0.01782429,0.000252237,0.002881232,0.00182206,0.002070947,0.09202798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002427327,"about_ca_system_score_gemma":0.005308364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1300345,"about_ca_topic_score_gemma":0.07587023,"domain_scores_codex":[0.9979302,0.0002755441,0.0003711304,0.0003920326,0.0006832327,0.0003478331],"domain_scores_gemma":[0.9930078,0.0008122268,0.0007587808,0.0004517624,0.004618621,0.0003507886],"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":[0.00003084537,0.00001031294,0.0008181282,0.0003466932,0.00001232873,0.000007337501,0.00002029209,0.00005567884,0.00001621628,0.0003875553,0.9957969,0.002497722],"study_design_scores_gemma":[0.0000942468,0.00001836663,0.01823695,0.0006814018,0.00002683683,0.00002979779,0.0002064422,0.0001061044,0.0001297543,0.0005270982,0.9799166,0.00002650147],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00008121695,0.00006114432,0.00003490474,0.00007553831,0.00006353972,0.00001395282,0.9984524,0.0000537911,0.001163451],"genre_scores_gemma":[0.0005348714,0.0001872525,0.0002556806,0.00007309754,0.00002433971,0.0001647961,0.9957902,0.00009634257,0.002873501],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1300345,"threshold_uncertainty_score":0.2757029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03740353099764809,"score_gpt":0.2956404501817674,"score_spread":0.2582369191841193,"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."}}