{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004291698,0.0002163294,0.0002886483,0.001917921,0.0009269168,0.001261164,0.0004342814,0.0004247927,0.003692104],"category_scores_gemma":[0.0008456865,0.0001399672,0.000337348,0.002598854,0.0006987646,0.0006349192,0.001658357,0.0004582945,0.000551299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001788958,"about_ca_system_score_gemma":0.002935645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04560521,"about_ca_topic_score_gemma":0.04400269,"domain_scores_codex":[0.9996246,0.00006152508,0.00004281745,0.0000638772,0.00005075657,0.0001564497],"domain_scores_gemma":[0.9997664,0.00002371238,0.00007744227,0.00001543687,0.00006947808,0.0000474369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001938124,0.00008950036,0.7635599,0.001211424,0.00015868,0.00308816,0.01567431,0.001336561,0.00112562,0.01417134,0.02202012,0.1773705],"study_design_scores_gemma":[0.000008383427,0.00008138738,0.9402565,0.0003401791,0.00002407677,0.0005581601,0.006437237,0.0002422254,0.0001833035,0.0007725035,0.05108136,0.00001465066],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9520813,0.0127768,0.0003108925,0.005603659,0.0002874115,0.00003272557,0.002620191,0.0001056996,0.02618146],"genre_scores_gemma":[0.9903736,0.004098535,0.0002209734,0.0005983754,0.0000862196,0.00003096411,0.001164108,0.000009781292,0.003417409],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04560521,"threshold_uncertainty_score":0.09067953,"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."}}