{"id":"W2323904577","doi":"10.1142/9789814417983_0003","title":"A MARKOV CHAIN MONTE CARLO SAMPLER FOR GENE GENEALOGIES CONDITIONAL ON HAPLOTYPE DATA","year":2013,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Markov chain Monte Carlo; Haplotype; Markov chain; Computer science; Statistics; Trait; Inference; Monte Carlo method; Mathematics; Biology; Artificial intelligence; Genetics; Gene","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":[0.00985061,0.0008764409,0.00229506,0.002390521,0.001636477,0.002409812,0.00466577,0.002465671,0.006903132],"category_scores_gemma":[0.03406065,0.002192898,0.002014078,0.002393642,0.003088442,0.003913366,0.003096584,0.004722661,0.001434548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001739723,"about_ca_system_score_gemma":0.004265358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01166186,"about_ca_topic_score_gemma":0.01647004,"domain_scores_codex":[0.9971728,0.001820705,0.0001103552,0.0004303443,0.0002995956,0.000166206],"domain_scores_gemma":[0.9601303,0.03482891,0.000727337,0.002254925,0.001232498,0.0008259819],"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":[0.0004247466,0.0001737798,0.004531389,0.000251628,0.000382171,0.0003234769,0.0003842472,0.5815529,0.001217149,0.3360186,0.005662413,0.06907744],"study_design_scores_gemma":[0.0001002895,0.0000150913,0.000180955,0.000034855,0.00003499088,0.00006768652,0.00001427191,0.8485982,0.000170216,0.1497875,0.0009723714,0.0000236403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006615932,0.0002177241,0.9918705,0.0001806383,0.00005066297,0.00006633305,0.0001756328,0.000314538,0.0005081302],"genre_scores_gemma":[0.2080237,0.0006986325,0.7824363,0.0004300465,0.0003772849,0.0009026278,0.001786062,0.0006135624,0.004731762],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01166186,"threshold_uncertainty_score":0.05209559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3437732647963233,"score_gpt":0.4264471285403374,"score_spread":0.08267386374401409,"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."}}