{"id":"W4412298327","doi":"","title":"Variance component estimation on Competing risk analysis with masked causes and gaussian random components: A simulation study.","year":2015,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Variance components; Component (thermodynamics); Statistics; Gaussian; Estimation; Variance (accounting); Econometrics; Mathematics; Gaussian random field; Computer science; Statistical physics; Gaussian process; Engineering; Economics; Physics","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":[0.00106109,0.0001762909,0.0004612317,0.0001346739,0.0001292641,0.00008595875,0.0000696521,0.00003576857,0.00004942387],"category_scores_gemma":[0.001650695,0.0001191911,0.00003175629,0.0003453116,0.00005602588,0.00007968536,0.00003534393,0.0001227431,0.000007682165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000419668,"about_ca_system_score_gemma":0.00001469186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003171112,"about_ca_topic_score_gemma":0.00008915384,"domain_scores_codex":[0.9982291,0.0005672168,0.0003704923,0.0002900507,0.0003831307,0.0001599856],"domain_scores_gemma":[0.9959239,0.003295227,0.0002394059,0.0002584151,0.0001408547,0.0001421353],"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":[0.004096389,0.004038652,0.3293717,0.0001775006,0.003179085,0.00005868947,0.01071711,0.4449723,0.0001165452,0.1840138,0.00006074294,0.01919746],"study_design_scores_gemma":[0.003240461,0.0003989975,0.1039235,0.00004133854,0.0007709114,0.000001139346,0.0007700287,0.8608535,0.00001640651,0.02980611,0.000005222262,0.0001723989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4315745,0.000003142379,0.5674291,0.00003492361,0.00001709815,0.0003074071,0.00000877104,0.00004882322,0.0005762051],"genre_scores_gemma":[0.6883507,9.239848e-7,0.3115614,0.0000230208,0.00001169037,0.00001361056,0.000007355638,0.000009240172,0.0000220223],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4158812,"threshold_uncertainty_score":0.486047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1105964337598668,"score_gpt":0.3836847767170067,"score_spread":0.2730883429571399,"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."}}