{"id":"W4385201802","doi":"10.30699/ijrrs.5.2.7","title":"Direct Quantile Function Estimation Using Information Principles and Its Applications in Reliability Analysis","year":2023,"lang":"en","type":"article","venue":"International Journal of Reliability Risk and Safety Theory and Application","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Principle of maximum entropy; Random variable; Mathematics; Quantile; Maximum entropy probability distribution; Entropy (arrow of time); Statistics; Akaike information criterion; Randomness; Maximum entropy spectral estimation; Order statistic; Kullback–Leibler divergence; Applied mathematics; Mathematical optimization","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":[0.009741414,0.0001167543,0.0002756829,0.0006267921,0.0001590604,0.0001224503,0.0002116104,0.00009166467,0.000008446039],"category_scores_gemma":[0.00418544,0.00008839346,0.00007890279,0.0009582463,0.0001063537,0.0008444575,0.00008622505,0.0001668454,0.000004844796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007509021,"about_ca_system_score_gemma":0.00004187108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002535804,"about_ca_topic_score_gemma":0.000004559601,"domain_scores_codex":[0.9978049,0.0003227501,0.0009868671,0.0002545606,0.0005256961,0.000105232],"domain_scores_gemma":[0.9959202,0.002577099,0.0006046449,0.0002073146,0.0006095322,0.00008124685],"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":[0.0006249817,0.00007249546,0.08017407,0.00003128067,0.0001009993,4.725937e-7,0.0008394326,0.7562686,0.0001760107,0.06731674,0.000009030497,0.09438589],"study_design_scores_gemma":[0.0003091071,0.00003521525,0.2705471,0.00001688115,0.000125423,0.000006450365,0.0003954286,0.6073768,0.0000625292,0.118931,0.002097839,0.00009629432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4748365,0.0001129726,0.5245707,0.0001370888,0.00006040057,0.0001881561,0.0000305959,0.00001666854,0.00004693195],"genre_scores_gemma":[0.9970137,0.0007567166,0.002108396,0.0000151403,0.00004005329,0.00001760065,0.0000263851,0.000003929521,0.00001800289],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5224623,"threshold_uncertainty_score":0.5010664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02547508138909881,"score_gpt":0.3224884705661056,"score_spread":0.2970133891770068,"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."}}