{"id":"W2091819253","doi":"10.1002/cjs.11226","title":"A semiparametric inverse‐Gaussian model and inference for survival data with a cured proportion","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health","keywords":"Inverse Gaussian distribution; Mathematics; Statistics; Applied mathematics; Estimator; Inference; Mixture model; Semiparametric regression; Gaussian process; Econometrics; Gaussian; Distribution (mathematics); Computer science; Artificial intelligence; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003545156,0.00011895,0.0003085103,0.0004779584,0.0001330197,0.0002942762,0.0006436444,0.0000533367,0.000030689],"category_scores_gemma":[0.01203156,0.00008492464,0.00001899635,0.0004601142,0.0002184832,0.0004267118,0.00003565495,0.0001510046,0.00000304487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007317578,"about_ca_system_score_gemma":0.001522316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006282701,"about_ca_topic_score_gemma":0.01006495,"domain_scores_codex":[0.9981647,0.0001741033,0.0005585787,0.0002507152,0.0006040115,0.0002478611],"domain_scores_gemma":[0.9963509,0.001364781,0.000507025,0.000404007,0.0006575297,0.0007157528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009144525,0.000151262,0.08574107,0.0001436441,0.0002664819,0.0003810743,0.005080806,0.03982024,0.001704407,0.2158253,0.103966,0.5460054],"study_design_scores_gemma":[0.0009062952,0.0007830155,0.003967727,0.00004243779,0.00005944741,0.0001120123,0.0005588094,0.9236773,0.00007100544,0.0653566,0.004242077,0.0002232776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01152907,0.00006503987,0.9868143,0.0001691138,0.0001822914,0.0001615176,0.0006522821,0.000002390899,0.0004240283],"genre_scores_gemma":[0.4452578,0.000004808727,0.554504,0.00006965835,0.00003401998,0.000001335632,0.00001052452,0.000009098296,0.0001088268],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8838571,"threshold_uncertainty_score":0.9962905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2351072959808381,"score_gpt":0.4259817070592473,"score_spread":0.1908744110784092,"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."}}