{"id":"W2001450464","doi":"10.1016/j.jspi.2008.05.016","title":"Planning life tests with progressively Type-I interval censored data from the lognormal distribution","year":2008,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Censoring (clinical trials); Mathematics; Log-normal distribution; Statistics; Interval (graph theory); Maximum likelihood; Interval estimation; Confidence interval; Econometrics; Combinatorics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01291803,0.0006774102,0.00091374,0.001256058,0.0003994963,0.001230099,0.00234808,0.001051808,0.006475955],"category_scores_gemma":[0.07227494,0.000526458,0.001296269,0.001203564,0.001621578,0.002876578,0.001465712,0.002199195,0.0004545373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001052098,"about_ca_system_score_gemma":0.002152581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00247877,"about_ca_topic_score_gemma":0.002783444,"domain_scores_codex":[0.9954793,0.002972941,0.0001863439,0.0005094921,0.0005873803,0.0002645225],"domain_scores_gemma":[0.9146741,0.07703624,0.002855152,0.002781283,0.001688915,0.0009643342],"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":[0.001294633,0.0002509048,0.02454141,0.0002821131,0.0002659197,0.0006803949,0.0004512946,0.6686201,0.001070289,0.1609367,0.00258251,0.1390236],"study_design_scores_gemma":[0.000101474,0.0005660509,0.00317158,0.00008160516,0.00007441708,0.000170042,0.0001624433,0.8059386,0.001670363,0.1868308,0.001190538,0.0000420283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1137479,0.0005027637,0.8801421,0.0005801375,0.00006392619,0.0002126563,0.0005623716,0.0003270711,0.003861101],"genre_scores_gemma":[0.7511565,0.0004264504,0.243144,0.0001586733,0.00006028871,0.0004741591,0.001630185,0.0001036577,0.002846131],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01291803,"threshold_uncertainty_score":0.06831795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2002180525713284,"score_gpt":0.4152842945483246,"score_spread":0.2150662419769962,"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."}}