{"id":"W3126114523","doi":"","title":"Population Decline in Lithuania: Who Lives in Declining Regions and Who Leaves?","year":2016,"lang":"en","type":"preprint","venue":"Data Archiving and Networked Services (DANS)","topic":"Urbanization and City Planning","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Commission","keywords":"Lithuanian; Census; Geography; Population; Quarter (Canadian coin); Socioeconomic status; Population decline; Inequality; Demographic economics; Socioeconomics; Demography; Development economics; Economics; Sociology","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":[0.001021674,0.0001795495,0.0003526023,0.001785567,0.001046354,0.00201843,0.0005276881,0.0004754148,0.002679319],"category_scores_gemma":[0.00218222,0.0001369492,0.0002449118,0.002681813,0.001093842,0.001607844,0.001693348,0.0008383009,0.0003444661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002216674,"about_ca_system_score_gemma":0.003441442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08025199,"about_ca_topic_score_gemma":0.08873162,"domain_scores_codex":[0.9994836,0.0001503015,0.00005806846,0.00006824392,0.00005619752,0.0001835784],"domain_scores_gemma":[0.9993176,0.0001498941,0.0002496947,0.00003218104,0.0001275258,0.0001230845],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001406288,0.00003770548,0.8210353,0.001223502,0.00007422586,0.001333281,0.03916067,0.0002131448,0.0002486062,0.003814127,0.01640189,0.1163169],"study_design_scores_gemma":[0.00000654527,0.00005261337,0.9012559,0.001099469,0.00003401879,0.0004298085,0.07382798,0.0002072935,0.0001173143,0.001320264,0.02162387,0.00002493097],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9457378,0.01929138,0.0002817603,0.0209957,0.000234339,0.00002796251,0.002802505,0.00004084285,0.01058771],"genre_scores_gemma":[0.9861659,0.00896362,0.0002517926,0.001688726,0.0001285737,0.000033003,0.001319173,0.000008317421,0.001440919],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08025199,"threshold_uncertainty_score":0.1595697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04208696308961502,"score_gpt":0.3224253646937123,"score_spread":0.2803384016040973,"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."}}