{"id":"W2267111803","doi":"","title":"EM-Based Likelihood Inference for Some Lifetime Distributions Based on Left Truncated and Right Censored Data and Associated Model Discrimination","year":2014,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Weibull distribution; Context (archaeology); Mathematics; Distribution (mathematics); Statistics; Inference; Computer science; Geography; Artificial intelligence; Mathematical analysis","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":[0.001225754,0.0001841301,0.0002248391,0.00008488008,0.0005046269,0.0001309691,0.0002247934,0.00009129015,0.00002194485],"category_scores_gemma":[0.00409587,0.0001616626,0.0000425212,0.0001138212,0.00008399999,0.0001974513,0.00003224072,0.0005122555,0.000004198858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003201565,"about_ca_system_score_gemma":0.0006462467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003480169,"about_ca_topic_score_gemma":0.0001165658,"domain_scores_codex":[0.9981329,0.0001117316,0.0003771999,0.0003057216,0.0002489616,0.0008234338],"domain_scores_gemma":[0.9979978,0.001049873,0.0002439771,0.0003133613,0.000235364,0.0001595855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003725121,0.0002785801,0.00007079775,0.00002147413,0.00003240938,1.07045e-7,0.00002050343,0.000227529,0.0001031357,0.9941365,0.0005139411,0.004557777],"study_design_scores_gemma":[0.0009088706,0.0001012361,0.0008826056,0.00002505186,0.00008505683,0.000003032437,0.00002551892,0.5291639,0.0000535331,0.4685779,0.00006761888,0.0001056612],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01728497,0.00002621102,0.9773856,0.002788145,0.00002084272,0.0003273413,0.002036525,0.00007080186,0.00005955316],"genre_scores_gemma":[0.987682,0.00003658281,0.009437766,0.0001678746,0.0000379056,0.00002974451,0.002514966,0.00002062366,0.00007246491],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9703971,"threshold_uncertainty_score":0.6592409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03050198261962134,"score_gpt":0.3284325686906452,"score_spread":0.2979305860710239,"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."}}