{"id":"W2119134978","doi":"10.1007/s11069-010-9560-3","title":"The inclusive and simplified forms of Bayesian interpolation for general and monotonic models using Gaussian and Generalized Beta distributions with application to Monte Carlo simulations","year":2010,"lang":"en","type":"article","venue":"Natural Hazards","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Uncertainty quantification; Monte Carlo method; Interpolation (computer graphics); Computer science; Mathematical optimization; Probabilistic logic; Monotonic function; Bayesian probability; Applied mathematics; Bayesian inference; Gaussian; Reliability (semiconductor); Beta distribution; Algorithm; Mathematics; Statistics; Machine learning; Artificial intelligence","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":[0.00009464121,0.00009055438,0.00009680188,0.00002255132,0.0003934823,0.00004606237,0.00005673751,0.00004618987,0.000001397799],"category_scores_gemma":[0.0000203939,0.00006090267,0.00001359659,0.00009155235,0.0001279562,0.0001486071,0.0001131799,0.00008476374,6.399041e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003498524,"about_ca_system_score_gemma":0.0000124103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006949789,"about_ca_topic_score_gemma":0.003751596,"domain_scores_codex":[0.9994298,0.000009740354,0.0001387558,0.0001828918,0.0001005908,0.0001382838],"domain_scores_gemma":[0.9996383,0.00006676067,0.00007228769,0.0001197667,0.00003176118,0.00007109895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005460539,0.00007168307,0.03493385,0.00007259214,0.0001476301,9.796896e-7,0.004460456,0.1030969,0.4079305,0.1995369,0.0001646289,0.2490378],"study_design_scores_gemma":[0.0003275132,0.00003571746,0.03085528,0.000005279132,0.00002664406,0.000004026545,0.00006758056,0.9592268,0.0003864376,0.008787291,0.0001854534,0.00009200376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8518026,0.00004180101,0.1471477,0.0003193697,0.0000264543,0.0004916597,0.0001225742,0.000007049269,0.00004083865],"genre_scores_gemma":[0.9797072,0.000008383838,0.02015229,0.00003236581,0.00001877754,0.00003074095,0.00002425541,0.000007543033,0.0000183969],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8561298,"threshold_uncertainty_score":0.3026388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006579967590821782,"score_gpt":0.2660403927628493,"score_spread":0.2594604251720275,"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."}}