{"id":"W4212869373","doi":"10.2139/ssrn.2159393","title":"A Parametric Bootstrap for Heavy Tailed Distributions","year":2011,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada; McGill University","keywords":"Heavy-tailed distribution; Econometrics; Parametric statistics; Statistics; Statistical physics; Mathematics; Environmental science; Probability distribution; 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":[],"consensus_categories":[],"category_scores_codex":[0.0006715863,0.00009822469,0.0001013173,0.0000438004,0.0002841483,0.00002531727,0.0001930687,0.00004162877,0.0003470329],"category_scores_gemma":[0.0001222729,0.00008714562,0.00008584771,0.0002467177,0.00006651199,0.0001015616,0.00003695934,0.0004787422,0.0001063929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000583765,"about_ca_system_score_gemma":0.0001854572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002047325,"about_ca_topic_score_gemma":0.0004840486,"domain_scores_codex":[0.9979888,0.00002247262,0.0001852652,0.0001555397,0.0001431342,0.001504771],"domain_scores_gemma":[0.9996336,0.00004658224,0.00009371011,0.0001107808,0.0000144117,0.0001009055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003029772,0.0006866414,0.08355247,0.00001298655,0.000320117,0.00001510236,0.0005803411,0.0003034313,0.0007552271,0.6880078,0.006551417,0.2189115],"study_design_scores_gemma":[0.0009343936,0.0006674529,0.03426434,0.000007834082,0.00006887678,0.0002728581,0.0005381778,0.0008478067,0.0002782487,0.9473354,0.01448529,0.000299343],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2286192,0.0004382521,0.762718,0.0002595221,0.0002633109,0.0003312134,0.00003732074,0.00004222179,0.00729099],"genre_scores_gemma":[0.9970559,0.0003312366,0.001753819,0.00004557714,0.00005790579,0.00002004661,0.000008244418,0.00001107832,0.0007161873],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7684367,"threshold_uncertainty_score":0.3799766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02500066351955646,"score_gpt":0.2492997645151921,"score_spread":0.2242991009956357,"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."}}