{"id":"W4394125298","doi":"10.6084/m9.figshare.12133575","title":"Model and code for \"The spatial structure of socioeconomic disadvantage: a Bayesian multivariate spatial factor analysis\"","year":2020,"lang":"en","type":"dataset","venue":"Figshare","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Multivariate statistics; Multivariate analysis; Bayesian probability; Disadvantage; Socioeconomic status; Computer science; Factor (programming language); Statistics; Econometrics; Code (set theory); Mathematics; Artificial intelligence; Sociology; Programming language; Demography; Set (abstract data type)","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00005392428,0.0004322114,0.001318196,0.0003054428,0.0001680719,0.0001339274,0.0007552689,0.0004013865,0.06936186],"category_scores_gemma":[0.0005099261,0.0003921044,0.0005999157,0.0002079312,0.00003149599,0.0001397351,0.0002846263,0.0003613696,0.000373581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006117796,"about_ca_system_score_gemma":0.00006677494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003356035,"about_ca_topic_score_gemma":0.01616055,"domain_scores_codex":[0.9978664,0.00002056882,0.0009189397,0.0008179458,0.00006537368,0.0003107667],"domain_scores_gemma":[0.9975086,0.000325018,0.001255659,0.0007146865,0.00005542459,0.0001406206],"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":[0.00005564612,0.00002406607,0.0003157087,0.0004230964,0.002056703,0.000001631442,0.00009413639,0.002960861,0.000004027924,0.00002283329,0.99362,0.0004212769],"study_design_scores_gemma":[0.0005081824,0.00004711666,0.003290748,0.00004778024,0.0006686797,5.298268e-7,0.00001143435,0.3318053,0.000009613642,0.0003972698,0.6627234,0.0004899736],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003758057,0.0003438809,0.00348282,0.000114363,0.0001053794,0.0005258914,0.9953707,0.00001432482,0.000005071254],"genre_scores_gemma":[0.04685812,0.00006916467,0.0001604091,0.0002032395,0.0003390028,0.00009279425,0.952204,0.0000343123,0.00003888592],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3308966,"threshold_uncertainty_score":0.9998531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0529677606401989,"score_gpt":0.2682644844057863,"score_spread":0.2152967237655874,"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."}}