{"id":"W4391308957","doi":"10.1038/s41597-023-02788-7","title":"Multidimensional well-being of US households at a fine spatial scale using fused household surveys","year":2024,"lang":"en","type":"article","venue":"Scientific Data","topic":"Urban, Neighborhood, and Segregation Studies","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Child Health and Human Development; National Science Foundation; RTI International; Canadian Institute for Advanced Research; University of Pennsylvania; U.S. Environmental Protection Agency","keywords":"Microdata (statistics); Survey data collection; Limiting; Scale (ratio); American Community Survey; General Social Survey; Government (linguistics); Consumer Expenditure Survey; Data science; Geography; Business; Environmental economics; Computer science; Public economics; Economics; Environmental health; Statistics; Engineering; Cartography; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.004750846,0.0001600975,0.0002520346,0.0001888874,0.001397088,0.0003367017,0.0007606266,0.00008955328,0.0004868068],"category_scores_gemma":[0.0003447772,0.0001453962,0.00009992191,0.001044265,0.001102294,0.0006143965,0.0008992545,0.0001237963,0.0001184116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001165612,"about_ca_system_score_gemma":0.0003850487,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.008491988,"about_ca_topic_score_gemma":0.02912385,"domain_scores_codex":[0.9968436,0.0004036063,0.0003899486,0.0008490793,0.001075815,0.0004379184],"domain_scores_gemma":[0.9982637,0.0004032024,0.0001088008,0.0009397562,0.0001478609,0.0001367006],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001005255,0.0007139403,0.3322464,0.000465918,0.0006975633,0.0001499848,0.06932683,0.001558215,0.04151652,0.00627876,0.5111859,0.03575944],"study_design_scores_gemma":[0.001154161,0.00005861313,0.05590463,0.000512764,0.0003229681,0.000008448251,0.002164537,0.06846923,0.005487263,0.0008369706,0.8640338,0.001046675],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9817569,0.001113323,0.00262092,0.0003818711,0.006250194,0.0003252672,0.002754409,0.0002639674,0.004533181],"genre_scores_gemma":[0.9878407,0.00003370233,0.001034268,0.00002257891,0.0003634196,0.000002084019,0.0006526661,0.00002433864,0.01002624],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3528478,"threshold_uncertainty_score":0.999903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1018774878751189,"score_gpt":0.3263173078517201,"score_spread":0.2244398199766012,"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."}}