{"id":"W2187436382","doi":"10.2308/jata.2006.28.1.1","title":"A Model of Dynamic Tax Planning with an Application to Estate Freezes","year":2006,"lang":"en","type":"article","venue":"Journal of the American Taxation Association","topic":"Corporate Taxation and Avoidance","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Database transaction; Deferral; Business; Transaction cost; Tax planning; Investment (military); Estate planning; Event (particle physics); Finance; Plan (archaeology); Real estate; Deferred tax; Time horizon; Estate; Economics; Microeconomics; Operations management; Computer science; Tax reform; Database; Double taxation; Public economics; Tax avoidance; State income tax","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004403203,0.00008580171,0.0001785082,0.0001938638,0.00009543968,0.00007775331,0.0001844676,0.0000188036,0.000003028229],"category_scores_gemma":[0.000425285,0.00006207591,0.00005459923,0.0007082096,0.00002378762,0.0006469302,0.00002240787,0.00009393615,0.000005729832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001838005,"about_ca_system_score_gemma":0.00004392127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006210147,"about_ca_topic_score_gemma":0.0005290891,"domain_scores_codex":[0.9989711,0.00002508899,0.0003745554,0.00009232667,0.0004317054,0.0001052327],"domain_scores_gemma":[0.9743567,0.00002819834,0.0249144,0.0001239834,0.0005666052,0.00001013949],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002183984,0.0001397308,0.4359069,0.00002027795,0.00003175087,4.458956e-7,0.0001808464,0.5079516,0.04065498,0.0011322,0.001692483,0.01207041],"study_design_scores_gemma":[0.0003634639,0.00004987079,0.7008312,0.00003895886,0.00005449134,9.770263e-7,0.0002782491,0.2952542,0.0003064895,0.002190105,0.0005180005,0.0001139413],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9334795,0.000002740539,0.06396514,0.001808399,0.00005233074,0.0001356432,0.000004204045,0.0000164675,0.0005356052],"genre_scores_gemma":[0.9956118,0.000001728965,0.003323306,0.0005837317,0.0001931944,0.000005514269,0.000008747235,0.00001269127,0.000259267],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2649244,"threshold_uncertainty_score":0.2531382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008735435197620298,"score_gpt":0.2286112534660652,"score_spread":0.2198758182684449,"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."}}