{"id":"W4388506640","doi":"10.59490/abe.2017.9.3624","title":"Population decline in Lithuania","year":2018,"lang":"en","type":"article","venue":"Architecture and the Built Environment","topic":"Urbanization and City Planning","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Commission","keywords":"Lithuanian; Geography; Census; Population; Quarter (Canadian coin); Population decline; Inequality; Demographic economics; Polarization (electrochemistry); Socioeconomics; Economic geography; Demography; Development economics; Sociology; Economics","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":[],"consensus_categories":[],"category_scores_codex":[0.0003749429,0.00004366819,0.0000594175,0.00002453906,0.0002424155,0.00002376507,0.00007896113,0.00003040989,0.0001278754],"category_scores_gemma":[0.00004003074,0.00002899572,0.00001274605,0.00006228723,0.0002787412,0.00002425937,0.00004621124,0.00006234567,0.00002557677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000017309,"about_ca_system_score_gemma":0.000004764273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002553498,"about_ca_topic_score_gemma":0.004945117,"domain_scores_codex":[0.9994631,0.000122246,0.00007951677,0.00009397362,0.0001331069,0.0001079964],"domain_scores_gemma":[0.9997993,0.00006206166,0.00002541544,0.0000800647,0.000002253515,0.00003089483],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002986134,0.0001108594,0.5723951,0.00000746486,0.00003166983,0.000005132148,0.1226602,0.0007730635,0.0002308937,0.1852426,0.0008074048,0.117437],"study_design_scores_gemma":[0.0009971081,0.00003588444,0.6019493,0.00001177482,0.00001095303,0.000001591131,0.0006756512,0.0002276226,0.00003457423,0.05501844,0.3409216,0.0001155726],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9731534,0.0002082522,0.001485301,0.01119828,0.00006744776,0.0001949663,0.000001175353,0.00001992509,0.01367125],"genre_scores_gemma":[0.9976037,0.00007833877,0.0002572081,0.001196154,0.0001715279,0.000004173918,0.00000356544,0.000003331595,0.0006819804],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3401142,"threshold_uncertainty_score":0.3860142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009514402044320872,"score_gpt":0.2516129937937565,"score_spread":0.2420985917494356,"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."}}