{"id":"W2756078004","doi":"10.5539/ijsp.v6n6p1","title":"A Bayes Inference for Step-Stress Accelerated Life Testing","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Minority Health and Health Disparities; Division of Civil, Mechanical and Manufacturing Innovation; National Institutes of Health; National Science Foundation","keywords":"Markov chain Monte Carlo; Accelerated life testing; Bayesian inference; Inference; Prior probability; Bayesian probability; Bayes factor; Computer science; Gibbs sampling; Bayes' theorem; Mathematics; Statistics; Algorithm; Artificial intelligence; Weibull distribution","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gpt","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"grok","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"},{"model":"opus","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008308464,0.00104033,0.001434983,0.001336501,0.0005530495,0.001363929,0.002327133,0.001093349,0.006323538],"category_scores_gemma":[0.03759537,0.0007863221,0.001223184,0.001142503,0.002211245,0.002300289,0.001779605,0.002779357,0.0008022723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001179442,"about_ca_system_score_gemma":0.00247093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003953244,"about_ca_topic_score_gemma":0.003363504,"domain_scores_codex":[0.9964715,0.002008066,0.000113377,0.000542372,0.0007036838,0.000161021],"domain_scores_gemma":[0.9765002,0.02003763,0.0008409327,0.000991636,0.001285613,0.0003440233],"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":[0.0002794437,0.00009758859,0.004153802,0.0002802697,0.0002025774,0.0003506797,0.0002505813,0.3756228,0.001378868,0.4692635,0.003947428,0.1441725],"study_design_scores_gemma":[0.00003312074,0.0000456745,0.0006942071,0.00004042741,0.0000407331,0.00009509984,0.00002108115,0.8058422,0.0005641235,0.1907562,0.001839096,0.00002799866],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003473857,0.0001469156,0.9955868,0.0001318329,0.00002136437,0.00002727265,0.00007676081,0.00007762774,0.0004575516],"genre_scores_gemma":[0.3459563,0.001355677,0.6444176,0.0003774115,0.0003591368,0.0005019808,0.0008892779,0.000215357,0.005927273],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008308464,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2841296077522858,"score_gpt":0.4611708686318423,"score_spread":0.1770412608795565,"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."}}