{"id":"W2970509656","doi":"10.1002/cjs.11502","title":"Spatial generalized linear mixed models in small area estimation","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Small area estimation; Generalized linear mixed model; Context (archaeology); Statistics; Sample size determination; Spatial analysis; Random effects model; Population; Parametric statistics; Generalized linear model; Scale (ratio); Mixed model; Computer science; Econometrics; Linear model; Estimation; Mathematics; Data mining; Geography; Cartography; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03056334,0.002443123,0.004108809,0.004014566,0.001010432,0.002802991,0.005176178,0.003023787,0.00513596],"category_scores_gemma":[0.06106478,0.00169963,0.004390639,0.005401794,0.002808722,0.002496687,0.003817813,0.004657386,0.001112086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002447173,"about_ca_system_score_gemma":0.002547325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02212097,"about_ca_topic_score_gemma":0.01873228,"domain_scores_codex":[0.9646948,0.03097554,0.0006721349,0.002116793,0.001023168,0.0005175303],"domain_scores_gemma":[0.9102551,0.08185092,0.002861077,0.002035717,0.00259364,0.0004035143],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001702914,0.0001228163,0.006761857,0.0004917067,0.001073481,0.0003255042,0.0003115951,0.7639581,0.000196363,0.1713643,0.002988882,0.05223519],"study_design_scores_gemma":[0.00003081592,0.00006340901,0.0005538344,0.00006444375,0.00008198477,0.0000291968,0.00006068908,0.9252093,0.00008940569,0.07197487,0.001812832,0.00002933479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005398066,0.00112992,0.991622,0.000477979,0.0001268129,0.0001352117,0.0003589632,0.000212037,0.0005389855],"genre_scores_gemma":[0.304299,0.003034046,0.6809763,0.0006495138,0.0006081903,0.002208754,0.001980467,0.0002094801,0.00603424],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03056334,"threshold_uncertainty_score":0.1616363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1344785433208908,"score_gpt":0.312277263610643,"score_spread":0.1777987202897522,"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."}}