{"id":"W4398172107","doi":"10.1007/s00180-024-01507-z","title":"Advancements in Rényi entropy and divergence estimation for model assessment","year":2024,"lang":"en","type":"article","venue":"Computational Statistics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Divergence (linguistics); Estimation; Econometrics; Entropy estimation; Mathematics; Statistics; Computer science; Artificial intelligence; Estimator; Engineering; Philosophy","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005497094,0.00007926561,0.0001097458,0.0001228389,0.0000636808,0.0001439864,0.0001105803,0.00002288813,0.00002464633],"category_scores_gemma":[0.0007021729,0.00006671634,0.00001508494,0.0001888284,0.00003907142,0.0001410718,0.00003954778,0.00005575343,0.00001321666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007527443,"about_ca_system_score_gemma":0.0001226659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001777399,"about_ca_topic_score_gemma":0.000001261184,"domain_scores_codex":[0.9988112,0.0000219536,0.000328112,0.0002616114,0.0004608368,0.0001162762],"domain_scores_gemma":[0.9977929,0.001910822,0.00003930417,0.00006978345,0.0001413338,0.00004587093],"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":[0.000003318955,0.00001061206,0.0001875423,0.0000177703,0.000003229286,0.000002113385,0.00005653706,0.6396001,0.000004286107,0.3391086,0.001885813,0.01912005],"study_design_scores_gemma":[0.00008931751,0.00001635941,0.002341473,0.00001290145,0.000003509234,8.240789e-7,0.0000065843,0.543085,5.044703e-7,0.4541933,0.0002075023,0.0000427684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001393696,0.0001485842,0.9974105,0.000135796,0.0002692195,0.0002053556,0.0003665778,0.00002525175,0.00004507191],"genre_scores_gemma":[0.398731,0.000007507215,0.6009981,0.00002332311,0.00001231077,0.0000220957,0.000053902,0.000004667027,0.0001470487],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3973373,"threshold_uncertainty_score":0.2720613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0889429360870464,"score_gpt":0.4218571900454962,"score_spread":0.3329142539584498,"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."}}