{"id":"W4408324355","doi":"10.21307/connections-2016-058","title":"The South Carolina Network Exchange Datasets","year":2016,"lang":"en","type":"article","venue":"Connections","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"South carolina; Computer science; Data science; Political science; Public administration","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001918585,0.0009314092,0.0005690698,0.006160365,0.001976209,0.001755885,0.003041242,0.001404818,0.02086702],"category_scores_gemma":[0.01518854,0.0003854164,0.0005195049,0.01134391,0.0005179881,0.001051715,0.002460849,0.00177725,0.01550658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002408998,"about_ca_system_score_gemma":0.004349899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2029348,"about_ca_topic_score_gemma":0.2552505,"domain_scores_codex":[0.9982089,0.0005601961,0.0001723658,0.000351999,0.0005094844,0.0001970164],"domain_scores_gemma":[0.9902968,0.002381926,0.0008716177,0.002198778,0.003477122,0.0007738412],"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":[0.00009206855,0.00005155768,0.005231185,0.0001774224,0.00004475113,0.0001236062,0.00017425,0.001309172,0.00008370874,0.003317445,0.9832065,0.006188165],"study_design_scores_gemma":[0.0002115068,0.00002860548,0.03337762,0.0004946864,0.00004602954,0.0001566319,0.0009855694,0.004313459,0.0004715126,0.005479578,0.954354,0.00008070043],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.006791287,0.0002323748,0.0006150446,0.0008438193,0.00005062375,0.0001699642,0.9854497,0.0004113057,0.00543583],"genre_scores_gemma":[0.007567237,0.0001924522,0.001446381,0.0001811437,0.00002819652,0.0006771173,0.9864406,0.0000739466,0.003392867],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2029348,"threshold_uncertainty_score":0.4035072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03224530369641598,"score_gpt":0.2192495951548504,"score_spread":0.1870042914584344,"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."}}