{"id":"W3005990175","doi":"10.3386/w24414","title":"Computerizing VAT Invoices in China","year":2018,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Taxation and Compliance Studies","field":"Economics, Econometrics and Finance","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"China; Business; Computer science; History; Archaeology","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":[0.001932257,0.0002974453,0.0003285771,0.001931016,0.0008097682,0.001185149,0.0006372179,0.0003560073,0.002526659],"category_scores_gemma":[0.00602854,0.0002537917,0.0002532923,0.00373895,0.0006641165,0.00115035,0.0006026197,0.0004130479,0.0005575083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004454571,"about_ca_system_score_gemma":0.004793647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1218826,"about_ca_topic_score_gemma":0.07548841,"domain_scores_codex":[0.997817,0.0003734283,0.000129697,0.0003419943,0.000989568,0.0003483141],"domain_scores_gemma":[0.9958045,0.001060535,0.0009516074,0.0007521554,0.001211382,0.0002198408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001180877,0.0008975124,0.4506842,0.0002880833,0.00007221429,0.001039756,0.002214366,0.1182261,0.01214229,0.01348043,0.0225768,0.3771974],"study_design_scores_gemma":[0.0002432881,0.0004747169,0.5902928,0.00006744156,0.0001026808,0.0003377532,0.001275637,0.3442835,0.01818386,0.004428006,0.04014315,0.0001672316],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9901956,0.0001959574,0.001515372,0.0002284906,0.00002125962,0.0001093933,0.0006535265,0.0004439668,0.006636487],"genre_scores_gemma":[0.9947442,0.0001314307,0.001439217,0.00001852694,0.000009900095,0.00002347308,0.0008920502,0.00002536371,0.002716054],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1218826,"threshold_uncertainty_score":0.2423462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5069840276242837,"score_gpt":0.4956700704585738,"score_spread":0.01131395716570999,"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."}}