{"id":"W2136541992","doi":"10.1080/00949655.2011.614245","title":"Estimation of a discriminant function from a mixture of two inverse Weibull distributions","year":2011,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of British Columbia; King Saud University","keywords":"Weibull distribution; Statistics; Mathematics; Discriminant function analysis; Monte Carlo method; Linear discriminant analysis; Inverse; Applied mathematics; Econometrics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006142267,0.0005970795,0.0009288968,0.001918626,0.0004377772,0.001142169,0.0009822681,0.0009712522,0.001404877],"category_scores_gemma":[0.01747197,0.0003629425,0.0006822645,0.0008699885,0.0009763996,0.001591624,0.001424845,0.001098343,0.0004336461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009689499,"about_ca_system_score_gemma":0.0004290614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001399428,"about_ca_topic_score_gemma":0.0008354799,"domain_scores_codex":[0.9985546,0.0007217387,0.00006722056,0.0002286874,0.0003526458,0.00007504734],"domain_scores_gemma":[0.99125,0.006491384,0.0006086084,0.0007619146,0.0007755529,0.0001125601],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0009284994,0.0002052725,0.0367915,0.0002190338,0.0001937623,0.0002268848,0.000439994,0.6645272,0.01849576,0.04285537,0.001110302,0.2340065],"study_design_scores_gemma":[0.0000123209,0.0000313285,0.002759531,0.00001831434,0.00001407545,0.00007232706,0.00003254404,0.9830376,0.003731668,0.009935671,0.0003287502,0.00002581418],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1436504,0.0001717799,0.8551527,0.0001541161,0.00001804887,0.00004505461,0.00004466956,0.0001747727,0.0005884655],"genre_scores_gemma":[0.8106869,0.00009499163,0.1880048,0.00003455524,0.00001499771,0.00006192593,0.0001310963,0.00004225451,0.0009284351],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006142267,"threshold_uncertainty_score":0.03248382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03798577552885073,"score_gpt":0.3179157665814056,"score_spread":0.2799299910525548,"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."}}