{"id":"W4409517104","doi":"10.1080/03610918.2025.2477712","title":"Inequalities and simulation methods for univariate log-concave densities","year":2025,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Point processes and geometric inequalities","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Univariate; Inequality; Statistics; Mathematics; Applied mathematics; Computer science; Statistical physics; Econometrics; Physics; Mathematical analysis; Multivariate statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001031559,0.0001467493,0.0002696101,0.0004624306,0.0003141184,0.0001286599,0.0001307912,0.00009216157,0.000003967402],"category_scores_gemma":[0.002578988,0.0001606147,0.00002161677,0.0004289939,0.0001489502,0.0001982813,0.0001505313,0.0001182781,4.052193e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006640048,"about_ca_system_score_gemma":0.00006473166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004428305,"about_ca_topic_score_gemma":0.00005271813,"domain_scores_codex":[0.9985939,0.0003623513,0.0006137518,0.0001946134,0.0000866877,0.0001487251],"domain_scores_gemma":[0.9821665,0.01682366,0.0002098426,0.0003079788,0.0004616789,0.00003035168],"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":[0.00003304438,0.00004660285,0.0002599678,0.0003085722,0.00002707028,1.008258e-7,0.002600716,0.09932982,0.000004890525,0.8049959,0.00004444134,0.09234889],"study_design_scores_gemma":[0.0004226159,0.00002136537,0.0008900918,0.00004391374,0.00002982985,2.724685e-7,0.0009698101,0.5341891,0.000005208498,0.4625899,0.0007526762,0.00008527548],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01122201,0.0006880867,0.9865559,0.0003053005,0.00008045611,0.0004662731,0.00008322499,0.00005952492,0.0005391978],"genre_scores_gemma":[0.5989279,0.00009292686,0.400493,0.0001024828,0.000008835707,0.00003401341,0.0001261086,0.000008916731,0.0002057836],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5877059,"threshold_uncertainty_score":0.6549676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2852750958994669,"score_gpt":0.5484491674627833,"score_spread":0.2631740715633164,"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."}}