{"id":"W2797963898","doi":"10.5281/zenodo.1217622","title":"Icpsr Census Metadata Repository","year":2018,"lang":"en","type":"article","venue":"Deep Blue (University of Michigan)","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Public Safety Research and Treatment","funders":"","keywords":"Metadata; Census; Computer science; Metadata repository; Geospatial metadata; World Wide Web; Data science; Database; Geography; Information retrieval; Meta Data Services; Population; Demography; Sociology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.006485679,0.0005634132,0.0009833833,0.01782939,0.002513624,0.005456262,0.003525649,0.00111631,0.2211856],"category_scores_gemma":[0.03678844,0.0008556964,0.0005118425,0.02849631,0.0005787274,0.004400044,0.004963903,0.00239986,0.1830975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003234413,"about_ca_system_score_gemma":0.02314698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05074364,"about_ca_topic_score_gemma":0.03520804,"domain_scores_codex":[0.9935522,0.001088235,0.000945877,0.0006864743,0.003380252,0.0003469473],"domain_scores_gemma":[0.9705306,0.003259466,0.00151511,0.005050974,0.01782159,0.001822156],"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":[0.000009668376,0.000008491781,0.0002486715,0.00008045122,0.00000261071,0.00001690397,0.00008773836,0.00004170126,0.00003256922,0.004083452,0.9813234,0.01406445],"study_design_scores_gemma":[0.000007785302,0.000002066966,0.0006144328,0.0000628168,0.000002731418,0.00002753044,0.0001100905,0.00007530012,0.00008297379,0.001140148,0.9978663,0.000007884542],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0006396563,0.0002624359,0.005128658,0.003154841,0.0006415505,0.0004724221,0.8641207,0.008511532,0.1170682],"genre_scores_gemma":[0.003851014,0.0008181404,0.01687555,0.001134635,0.0003423093,0.001106268,0.9321284,0.003216425,0.04052713],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2211856,"threshold_uncertainty_score":0.7399395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01540445247018261,"score_gpt":0.2265739975964951,"score_spread":0.2111695451263125,"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."}}