{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003768615,0.0004034935,0.0005493247,0.0004133378,0.0003285006,0.0002959979,0.0008960309,0.0002358501,0.0002142746],"category_scores_gemma":[0.0000452841,0.000490432,0.0005400773,0.002131133,0.0002392986,0.0006899933,0.002113011,0.0005780165,0.0001961728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002506795,"about_ca_system_score_gemma":0.0004623643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000174461,"about_ca_topic_score_gemma":0.00004457605,"domain_scores_codex":[0.9971744,0.0001602714,0.0006592722,0.001418884,0.0002282303,0.0003588892],"domain_scores_gemma":[0.9974489,0.0001924588,0.0007106967,0.0008723904,0.0006082814,0.0001672255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001533908,0.00007526486,0.0000995871,0.0001049086,0.0001876947,0.00003806828,0.0009962317,0.5757518,0.0000181514,0.4218935,0.00004227625,0.000777146],"study_design_scores_gemma":[0.0001942573,0.00006916377,0.001088823,0.0002179627,0.0002789418,0.000005561789,0.0002419366,0.8543971,0.00002003292,0.1430052,0.0001072993,0.0003737253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09052797,0.0001593611,0.9052096,0.0001874349,0.0008750906,0.0002250693,0.000114408,0.00009708396,0.002603956],"genre_scores_gemma":[0.9908046,0.00007427303,0.005808596,0.00001401617,0.0001309712,0.000001030903,0.00007607882,0.00002235071,0.003068058],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9002767,"threshold_uncertainty_score":0.9997547,"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."}}