{"id":"W4405434096","doi":"10.48550/arxiv.2412.10370","title":"Computational Explorations of Total Variation Distance","year":2024,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Science and Engineering Research Board; National Research Foundation Singapore; National Research Foundation; National Science Foundation","keywords":"Variation (astronomy); Statistics; Computer science; Mathematics; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.006877613,0.0011852,0.001878153,0.001974915,0.001587458,0.004437597,0.004289283,0.001795441,0.005529868],"category_scores_gemma":[0.04927246,0.0009692056,0.001782157,0.002145629,0.004778943,0.0103368,0.006143156,0.004325649,0.0005551401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002780216,"about_ca_system_score_gemma":0.001912353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002385898,"about_ca_topic_score_gemma":0.003013356,"domain_scores_codex":[0.9940486,0.003138497,0.0002108316,0.001298714,0.0009275146,0.0003758543],"domain_scores_gemma":[0.9452755,0.04849975,0.001199756,0.003461423,0.000865624,0.0006979232],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001909987,0.00006826727,0.001793875,0.0002478051,0.0001107426,0.0001123125,0.0002524619,0.1388876,0.0005254506,0.8182166,0.002908465,0.03668546],"study_design_scores_gemma":[0.00002070319,0.00002937952,0.0001283382,0.0000219628,0.00001214592,0.00004067405,0.00002865289,0.3636616,0.0002532537,0.6347848,0.001003588,0.00001480943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07828011,0.003002698,0.893125,0.00550485,0.0002086493,0.00008914314,0.0004289256,0.0007519635,0.01860856],"genre_scores_gemma":[0.7968429,0.001355148,0.1949722,0.0008748506,0.0004099769,0.0002332727,0.0006551628,0.0004296491,0.004226731],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006877613,"threshold_uncertainty_score":0.03637272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06228942133737303,"score_gpt":0.1792983960371453,"score_spread":0.1170089746997723,"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."}}