{"id":"W2133331977","doi":"10.31274/etd-180810-1709","title":"A Small Area Procedure for Estimating Population Counts","year":2010,"lang":"en","type":"dissertation","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Estimator; Contingency table; Statistics; Small area estimation; Mathematics; Econometrics; Table (database); Mean squared error; Sample (material); Sample size determination; Population; Geography; Computer science; Demography; Data mining","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":[0.0001393499,0.0001297697,0.0002029282,0.00003753081,0.00005533167,0.00003814941,0.00007387631,0.0002563063,0.0005414609],"category_scores_gemma":[0.00281368,0.0001055289,0.00004689815,0.00003823786,0.000005186837,0.00001694788,0.000003708948,0.0001738395,0.00001597483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001288441,"about_ca_system_score_gemma":0.00004544075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001259183,"about_ca_topic_score_gemma":0.0001870481,"domain_scores_codex":[0.9993945,0.000009579756,0.0002173491,0.0001658421,0.0000968689,0.0001158898],"domain_scores_gemma":[0.9990295,0.0005464088,0.000151329,0.0001151701,0.000124648,0.00003301308],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002024764,0.0000346798,0.00004833012,0.001789692,0.00001769943,6.462364e-7,0.0001381745,8.535903e-8,0.00009874054,0.8294049,0.005082073,0.1633648],"study_design_scores_gemma":[0.00007817676,0.00002016188,0.0003769847,0.0002083133,0.00006323872,0.000001368348,0.00003735764,0.01136653,0.0001016661,0.9873453,0.0002383467,0.0001625477],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001488953,0.000005672587,0.9574803,0.00001206754,0.0007317206,0.0004665997,0.00004595123,0.00007751099,0.0396912],"genre_scores_gemma":[0.001017071,4.936558e-7,0.9892628,0.00001993325,0.0001531799,0.00009894863,0.0004639646,0.00003117455,0.008952472],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1632022,"threshold_uncertainty_score":0.5928618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09662603949932043,"score_gpt":0.4030580495697122,"score_spread":0.3064320100703918,"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."}}