{"id":"W2006213144","doi":"10.1139/f05-224","title":"The Gibbs and splitmerge sampler for population mixture analysis from genetic data with incomplete baselines","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mixture model; Gibbs sampling; Statistics; Markov chain; Mathematics; Population; Markov chain Monte Carlo; Merge (version control); Bayesian probability; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.01122033,0.001522043,0.002167664,0.003644913,0.001567053,0.002114289,0.00364563,0.001907792,0.004716605],"category_scores_gemma":[0.03256445,0.001604144,0.002626174,0.002944561,0.002242528,0.002810017,0.003138438,0.005276366,0.001543953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001691272,"about_ca_system_score_gemma":0.003723532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01352603,"about_ca_topic_score_gemma":0.01988381,"domain_scores_codex":[0.9957215,0.003029141,0.0001515013,0.0004525,0.0005084485,0.0001368853],"domain_scores_gemma":[0.9873908,0.01067165,0.0003416056,0.0007730906,0.0006131085,0.0002097527],"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":[0.0002907871,0.0001233523,0.003395845,0.0003515983,0.0005565758,0.000235523,0.0004225367,0.4769596,0.001912801,0.2677384,0.006501463,0.2415116],"study_design_scores_gemma":[0.00004149857,0.00001667611,0.0002601385,0.00003156046,0.00003028486,0.00005511758,0.00001694179,0.8875475,0.0003412963,0.1091394,0.00248519,0.00003438906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007040309,0.0001521682,0.9986178,0.00004280655,0.00001560536,0.00004031349,0.00004478224,0.0002541397,0.0001284681],"genre_scores_gemma":[0.02852216,0.00049622,0.9678338,0.0001021454,0.0001111378,0.0007134503,0.0006619268,0.0003013489,0.001257858],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01352603,"threshold_uncertainty_score":0.05933952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02999587880155407,"score_gpt":0.2541209905354787,"score_spread":0.2241251117339246,"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."}}