{"id":"W2043129860","doi":"10.1023/b:lida.0000036389.14073.dd","title":"Covariates and Random Effects in a Gamma Process Model with Application to Degradation and Failure","year":2004,"lang":"en","type":"article","venue":"Lifetime Data Analysis","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":572,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gamma process; Covariate; Degradation (telecommunications); Process (computing); Random effects model; Econometrics; Goodness of fit; Computer science; Statistics; Sequence (biology); Mathematics; Chemistry","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":[0.0001603878,0.00009406773,0.0001869783,0.0001692938,0.0000275242,0.00004738765,0.00009579647,0.00004385886,0.000001119646],"category_scores_gemma":[0.00005205612,0.00007998712,0.000009588419,0.0007404319,0.00001680181,0.0003265085,0.00002844955,0.00005107454,0.000001699042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002255903,"about_ca_system_score_gemma":0.00001087129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001164506,"about_ca_topic_score_gemma":0.0007568509,"domain_scores_codex":[0.999404,0.00001133623,0.0001297093,0.0002687975,0.00008457008,0.0001015542],"domain_scores_gemma":[0.9995509,0.00003074995,0.00002218687,0.0003157713,0.00002882191,0.00005153125],"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":[0.0000369915,0.00001273254,0.00196491,0.00007619752,0.00008507357,4.465893e-7,0.0001885697,0.9963082,0.0002379696,0.0001500401,0.00001442219,0.0009244436],"study_design_scores_gemma":[0.0008466626,0.00001364898,0.002156567,0.00002706765,0.0002821693,8.714651e-7,0.00003350587,0.9958894,0.0001883595,0.0004384661,0.00001733278,0.000105894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1500194,0.00005931308,0.8493059,0.0002336194,0.000002257802,0.0002850668,0.00003442829,0.0000433154,0.00001666721],"genre_scores_gemma":[0.945764,0.00007085493,0.05323378,0.00003121317,0.000006937005,0.00006949979,0.0008078386,0.000009647024,0.000006238402],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7960722,"threshold_uncertainty_score":0.326178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003535797549385565,"score_gpt":0.210394489702789,"score_spread":0.2068586921534034,"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."}}