{"id":"W2979218902","doi":"","title":"Turning Standards into Rules Part 1: Using Machine Learning to Predict Tax Outcomes","year":2018,"lang":"en","type":"article","venue":"TSpace","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Rule of thumb; Field (mathematics); Order (exchange); Computer science; Compliance (psychology); Outcome (game theory); Artificial intelligence; Business; Psychology; Economics","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":[0.004516702,0.0007369579,0.0006719774,0.002609615,0.0005548848,0.004657211,0.001057333,0.0009797586,0.007635061],"category_scores_gemma":[0.02798379,0.0003781212,0.0006517436,0.002512842,0.001021211,0.002883221,0.001105799,0.002383907,0.00241229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001298648,"about_ca_system_score_gemma":0.001867124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01290749,"about_ca_topic_score_gemma":0.009052449,"domain_scores_codex":[0.9968898,0.001647706,0.0002203834,0.0004685077,0.0006217092,0.0001519107],"domain_scores_gemma":[0.989877,0.006023202,0.001124024,0.001474663,0.001320254,0.0001809076],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005784433,0.001088428,0.1655462,0.0003232052,0.0003991314,0.0001110434,0.000716343,0.144448,0.0013954,0.06974106,0.03705628,0.5785965],"study_design_scores_gemma":[0.0001215598,0.0003507994,0.06080741,0.0004139269,0.0001711345,0.00008676892,0.0004720233,0.6885545,0.00686951,0.2260402,0.01601523,0.00009694802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5603203,0.002868786,0.32267,0.009388126,0.0006931397,0.001134031,0.01062243,0.001755785,0.0905474],"genre_scores_gemma":[0.9300063,0.0007286748,0.05636322,0.0002930015,0.0001769383,0.0002681828,0.00517115,0.0001026349,0.006889838],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01290749,"threshold_uncertainty_score":0.02566469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06745830783268637,"score_gpt":0.4373483669076771,"score_spread":0.3698900590749908,"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."}}