{"id":"W2185560772","doi":"","title":"USE OF TAX DATA: AN APPLICATION OF GOODS AND SERVICES TAX (GST) DATA","year":2003,"lang":"en","type":"article","venue":"","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Imputation (statistics); Certification; Missing data; Computer science; Data collection; Goods and services; Database; Data science; Business; Economics; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.007031461,0.0002887457,0.0004755861,0.004513078,0.001640947,0.002461651,0.001380816,0.0006602433,0.006575895],"category_scores_gemma":[0.03761915,0.0004553667,0.0004976998,0.0228115,0.0006483028,0.00119892,0.001813558,0.001020912,0.001553312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008564305,"about_ca_system_score_gemma":0.024698,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7306126,"about_ca_topic_score_gemma":0.7096462,"domain_scores_codex":[0.9926637,0.002143427,0.0004964946,0.0003668768,0.003912715,0.0004166841],"domain_scores_gemma":[0.9866916,0.003656849,0.0009107428,0.002697311,0.005540556,0.0005030506],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001392554,0.0001342329,0.1250632,0.0005373026,0.000153062,0.0003623369,0.003792359,0.009323799,0.0005679565,0.04272039,0.2502148,0.5669914],"study_design_scores_gemma":[0.00006552641,0.00007202793,0.1490091,0.0004372393,0.0001087501,0.0004787495,0.003984903,0.02503887,0.00288274,0.01820137,0.7996022,0.0001185108],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1472521,0.004686825,0.2672455,0.04051661,0.001005793,0.005916544,0.2952891,0.00735935,0.2307281],"genre_scores_gemma":[0.4856676,0.007663369,0.3943631,0.001708845,0.0003577574,0.001495054,0.0770343,0.001435916,0.03027408],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2693874,"threshold_uncertainty_score":0.5419478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3377820052115862,"score_gpt":0.4234950074182489,"score_spread":0.08571300220666261,"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."}}