{"id":"W2006344350","doi":"10.1080/10485252.2011.559547","title":"Exact nonparametric inference for component lifetime distribution based on lifetime data from systems with known signatures","year":2011,"lang":"en","type":"article","venue":"Journal of nonparametric statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministerio de Ciencia y Tecnología; Chinese University of Hong Kong; University of Hong Kong","keywords":"Nonparametric statistics; Inference; Mathematics; Component (thermodynamics); Statistics; Statistical inference; Econometrics; Fiducial inference; Applied mathematics; Computer science; Frequentist inference; Artificial intelligence; Bayesian inference; Bayesian probability","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":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001911605,0.0004752147,0.001165781,0.0006617597,0.0001525592,0.0001803135,0.001167711,0.0002465421,0.0002702862],"category_scores_gemma":[0.03616213,0.0003418235,0.0001121607,0.001380386,0.0001964684,0.0001945012,0.0001187709,0.0007506698,0.00002515588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002045918,"about_ca_system_score_gemma":0.0004195186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000151246,"about_ca_topic_score_gemma":0.000004134678,"domain_scores_codex":[0.9956673,0.0004726576,0.001537189,0.0005269438,0.001258989,0.0005368871],"domain_scores_gemma":[0.9626588,0.0327289,0.001839826,0.001049291,0.001291882,0.0004312885],"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":[0.01695702,0.01694219,0.01749311,0.002799609,0.003522603,0.001335451,0.0008205592,0.006833864,0.0003608047,0.4648663,0.3122262,0.1558423],"study_design_scores_gemma":[0.005142439,0.008882407,0.03383942,0.001290514,0.001695577,0.00005193726,0.0001393324,0.8416935,0.0004465155,0.1024297,0.00314206,0.001246605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005676266,0.000317797,0.9789028,0.00003011763,0.0008087563,0.0006676396,0.01324407,0.00003576381,0.0003168003],"genre_scores_gemma":[0.3623536,0.00004531403,0.6369177,0.00005914034,0.0002209395,0.00001516266,0.0003145559,0.00004516611,0.00002848539],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8348596,"threshold_uncertainty_score":0.9999034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1342693788779543,"score_gpt":0.3646284393817283,"score_spread":0.230359060503774,"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."}}