{"id":"W2040494026","doi":"10.1081/sta-100105705","title":"PROGRESSIVE INTERVAL CENSORING: SOME MATHEMATICAL RESULTS WITH APPLICATIONS TO INFERENCE","year":2001,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":122,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Censoring (clinical trials); Inference; Interval estimation; Statistics; Sample (material); Sample size determination; Point estimation; Confidence interval; Mathematics; Computer science; Econometrics; Artificial intelligence","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":[0.02071507,0.001753702,0.001591142,0.004054072,0.001434959,0.003548275,0.003217443,0.002831218,0.004196631],"category_scores_gemma":[0.06437977,0.001000814,0.002771258,0.005756493,0.007100061,0.008232445,0.003676963,0.007155258,0.001719523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002113719,"about_ca_system_score_gemma":0.001848327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002045685,"about_ca_topic_score_gemma":0.001244544,"domain_scores_codex":[0.9928515,0.003492841,0.0004847033,0.0008791361,0.002029406,0.0002625276],"domain_scores_gemma":[0.9546353,0.03811979,0.001859079,0.002856382,0.002098301,0.0004313149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001814678,0.00003102881,0.0005749213,0.0002294967,0.00006909805,0.0002107772,0.0002743637,0.01614112,0.000231471,0.946176,0.00274157,0.0333021],"study_design_scores_gemma":[0.00001256725,0.00003460708,0.0002977057,0.0001280495,0.0000378337,0.0002626683,0.0000498075,0.06306295,0.0003281191,0.9247869,0.01096362,0.00003510276],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007818711,0.002882581,0.991867,0.0009894704,0.0001291947,0.00002219546,0.00007552125,0.00005493009,0.003197131],"genre_scores_gemma":[0.1059963,0.02127785,0.8570676,0.002419393,0.00391485,0.0005919565,0.0005615393,0.0002542991,0.007916266],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02071507,"threshold_uncertainty_score":0.1095531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1556839418083917,"score_gpt":0.4954121019464551,"score_spread":0.3397281601380634,"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."}}