{"id":"W2939259040","doi":"10.48550/arxiv.1904.07998","title":"SynC: A Unified Framework for Generating Synthetic Population with Gaussian Copula","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Copula (linguistics); sync; Gaussian; Econometrics; Computer science; Population; Statistical physics; Mathematics; Statistics; Physics; Telecommunications; 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":[0.004818317,0.0008721614,0.0009087235,0.001792553,0.0006098014,0.001549049,0.002346594,0.001155113,0.004149585],"category_scores_gemma":[0.0182571,0.0006843974,0.001520026,0.00180316,0.0008220467,0.001449888,0.00231203,0.001968422,0.001043417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0010123,"about_ca_system_score_gemma":0.002150982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008255572,"about_ca_topic_score_gemma":0.008465376,"domain_scores_codex":[0.9983385,0.0008875015,0.0000904542,0.0002896965,0.0003086539,0.00008527098],"domain_scores_gemma":[0.9945564,0.003110541,0.000399097,0.0008919265,0.0008636871,0.0001783128],"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":[0.00006621212,0.00008750683,0.006103327,0.0001320698,0.0001744084,0.0001567859,0.0002368848,0.7753919,0.001116868,0.1321819,0.01021117,0.07414101],"study_design_scores_gemma":[0.00001067268,0.00001086725,0.0003226085,0.00001001228,0.000005953909,0.00002252796,0.00001609758,0.9649364,0.0003020251,0.0314773,0.002873203,0.00001227167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00209064,0.00003944374,0.9961218,0.00008557698,0.00002277245,0.00005917911,0.000556268,0.0006154064,0.0004089048],"genre_scores_gemma":[0.1317303,0.0002486464,0.8591304,0.0002576077,0.0001147797,0.0009714026,0.005304821,0.0007537277,0.00148831],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008255572,"threshold_uncertainty_score":0.025482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2036129619334166,"score_gpt":0.2808099554365962,"score_spread":0.0771969935031796,"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."}}