{"id":"W2166179725","doi":"10.3390/e17064040","title":"A Penalized Likelihood Approach to Parameter Estimation with Integral Reliability Constraints","year":2015,"lang":"en","type":"article","venue":"Entropy","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reliability (semiconductor); Inference; Maximum likelihood; Mathematics; Applied mathematics; Stress (linguistics); Computer science; Statistics; Mathematical optimization; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008027456,0.0009206232,0.001404218,0.001750143,0.0004548977,0.001603473,0.002453177,0.001356625,0.002628958],"category_scores_gemma":[0.03102958,0.000948029,0.001039333,0.00165326,0.001647413,0.00222601,0.002017145,0.002676624,0.0005313331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001035918,"about_ca_system_score_gemma":0.001633362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002859881,"about_ca_topic_score_gemma":0.002176987,"domain_scores_codex":[0.9961962,0.002680265,0.0001339622,0.0003855072,0.0004867545,0.0001173564],"domain_scores_gemma":[0.979791,0.01750124,0.0008255593,0.0007945442,0.0008893001,0.0001983576],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001213048,0.00007605917,0.001828962,0.0002309069,0.0001723086,0.0002462985,0.0001995792,0.7504843,0.001445204,0.167598,0.00197422,0.0756228],"study_design_scores_gemma":[0.00001012074,0.00001699626,0.0001636571,0.00001289263,0.000009302637,0.00003584926,0.000007361169,0.9673405,0.0002595265,0.03143023,0.0007015032,0.00001214803],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001774694,0.0001077054,0.99766,0.000111437,0.00000854379,0.00001347546,0.00002952809,0.0000537418,0.0002408837],"genre_scores_gemma":[0.1932331,0.0006771956,0.8008784,0.0002157341,0.0002205866,0.0003501587,0.0005273445,0.000191267,0.003706128],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008027456,"threshold_uncertainty_score":0.04245371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06958829811114592,"score_gpt":0.3467598586862424,"score_spread":0.2771715605750965,"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."}}