{"id":"W7113185159","doi":"","title":"A roma népesség számának prognózisa, a romák által felülreprezentált megyékben 2061-ig = Projection of Roma population in overrepresented counties until 2061","year":2020,"lang":"hu","type":"article","venue":"Repository of the Academy's Library (Library of the Hungarian Academy of Sciences)","topic":"Romani and Gypsy Studies","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Population; Projection (relational algebra); Quarter (Canadian coin); Inequality; Population projection; Demographic analysis","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","sts","research_integrity"],"consensus_categories":["sts","research_integrity"],"category_scores_codex":[0.001508359,0.0008796215,0.002059276,0.000662496,0.001673237,0.00009535165,0.005217406,0.001466452,0.0002139617],"category_scores_gemma":[0.0005519777,0.0006289734,0.0009830008,0.004503556,0.003450895,0.006667868,0.004423039,0.002992211,0.00001060135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008592827,"about_ca_system_score_gemma":0.0009954649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007479992,"about_ca_topic_score_gemma":0.000007684706,"domain_scores_codex":[0.9869963,0.00331485,0.004359431,0.001561938,0.002590699,0.001176812],"domain_scores_gemma":[0.9916371,0.0009408767,0.006278557,0.0007441932,0.00006976932,0.0003295408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00163588,0.0006879067,0.9068043,0.004323103,0.0005657625,0.000003476533,0.01294172,0.0007857807,0.03138667,0.009796155,0.03062654,0.0004427086],"study_design_scores_gemma":[0.001763624,0.00046971,0.8956991,0.004815769,0.0003493625,0.00002197642,0.004319271,0.005110228,0.07928226,0.004331559,0.003218248,0.0006189097],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9303042,0.005275163,0.00000345639,0.04975908,0.001415143,0.004009803,0.0002832972,0.0001594621,0.008790391],"genre_scores_gemma":[0.9902524,0.0006334532,0.0006554249,0.001407573,0.0007821756,0.0000692206,0.00001453598,0.0001165413,0.006068703],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05994817,"threshold_uncertainty_score":0.9998298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05341672690463765,"score_gpt":0.3166830911554457,"score_spread":0.263266364250808,"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."}}