{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001280293,0.0003480443,0.0005388224,0.0005316531,0.0003209361,0.0003516616,0.0008841545,0.0004786756,0.00006899112],"category_scores_gemma":[0.0006315784,0.0003020379,0.0002868241,0.0008361684,0.00008091694,0.000239749,0.000260148,0.0004857155,0.00007902535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001104183,"about_ca_system_score_gemma":0.0001343814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002415607,"about_ca_topic_score_gemma":0.0001696426,"domain_scores_codex":[0.9970443,0.0002470663,0.0005049148,0.00144357,0.0003891398,0.0003710511],"domain_scores_gemma":[0.9962358,0.001018817,0.0007685529,0.001362346,0.0004667464,0.0001476625],"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":[0.0001529194,0.00003976576,0.0107595,0.00004814034,0.00005693113,0.00001418106,0.0001732371,0.8663644,0.00001088179,0.1211785,0.00007854189,0.001122967],"study_design_scores_gemma":[0.0003270259,0.00007997941,0.0019218,0.0002570135,0.000137003,0.000002517912,0.0007122552,0.7543906,0.000007927306,0.2417175,0.0000853527,0.0003610432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4659589,0.00002413112,0.5325128,0.0000746547,0.0004434237,0.0004248338,0.00002921317,0.00007294074,0.0004590898],"genre_scores_gemma":[0.9836443,0.0000341294,0.01430526,0.00008541925,0.00009862354,0.000004527065,0.0001157316,0.0000396617,0.001672329],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5182076,"threshold_uncertainty_score":0.9999432,"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."}}