{"id":"W2039911323","doi":"10.1109/tr.2012.2208301","title":"Multiple-Stress Model for One-Shot Device Testing Data Under Exponential Distribution","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Jackknife resampling; Expectation–maximization algorithm; Mathematics; Reliability (semiconductor); Parametric statistics; Fisher information; Exponential family; Exponential function; Accelerated life testing; Exponential distribution; Algorithm; Inference; Maximization; Likelihood function; Statistics; Convergence (economics); Computer science; Estimation theory; Mathematical optimization; Maximum likelihood; Weibull distribution; 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":[0.0005932846,0.0002008617,0.000224236,0.00003232792,0.0004667822,0.00004196893,0.0002969904,0.0001349552,0.0000847469],"category_scores_gemma":[0.001019108,0.0002053674,0.00008887513,0.0002591435,0.0001227196,0.0003581777,0.000004837082,0.0002291998,0.0000346197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001864982,"about_ca_system_score_gemma":0.00007011661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003219511,"about_ca_topic_score_gemma":0.00003143524,"domain_scores_codex":[0.9982728,0.00007583763,0.0005162013,0.0004437313,0.0002925109,0.0003989129],"domain_scores_gemma":[0.9957121,0.002617183,0.000131537,0.001022444,0.0002971025,0.0002196139],"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":[0.001219136,0.03890178,0.001676657,0.002511108,0.0005230739,7.60834e-7,0.001232202,0.3846803,0.01194569,0.4473755,0.01575788,0.09417595],"study_design_scores_gemma":[0.0006422334,0.00002613634,0.002077632,0.0000374037,0.0001888484,0.000001608848,0.00006055301,0.9740434,0.004340216,0.01814285,0.0001819788,0.000257097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01786121,0.000004912889,0.9608981,0.0004104665,0.000191106,0.0008718728,0.01939593,0.0002464901,0.0001199036],"genre_scores_gemma":[0.901651,0.000002083864,0.09662041,0.00007137418,0.00006304356,0.0003585384,0.00115541,0.00001899097,0.00005909705],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8837898,"threshold_uncertainty_score":0.8374639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4255242297670597,"score_gpt":0.4233966779995645,"score_spread":0.002127551767495128,"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."}}