{"id":"W2168047871","doi":"10.1093/bioinformatics/btq355","title":"TreesimJ: a flexible, forward time population genetic simulator","year":2010,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; McGill University; National Science Foundation","keywords":"Computer science; Population; Simulation; Genetic algorithm; Machine learning; Demography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007838637,0.000150344,0.0001204958,0.0000627783,0.000119462,0.00004886586,0.0001905291,0.0002387711,0.0002144718],"category_scores_gemma":[0.0000579523,0.0001444053,0.00008909203,0.00009703553,0.00003935229,0.00001007678,0.00008957525,0.0001050285,0.0002264391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000567962,"about_ca_system_score_gemma":0.00003351093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001062364,"about_ca_topic_score_gemma":0.0000174957,"domain_scores_codex":[0.9992009,0.00001226656,0.0002649427,0.0001391268,0.0001780214,0.0002047913],"domain_scores_gemma":[0.9993185,0.000006848578,0.0001180507,0.0003689865,0.00007233067,0.0001153211],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0004801006,0.00027679,0.3153211,0.0004168729,0.0004773735,0.000009351365,0.001629148,0.03064653,0.395649,0.002064691,0.0782968,0.1747323],"study_design_scores_gemma":[0.002583174,0.0004915023,0.494038,0.00002035863,0.0001459995,0.0000997735,0.0001617474,0.06675063,0.03274441,0.001002855,0.400657,0.001304507],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926137,0.00002550788,0.003855909,0.00004806284,0.0003498723,0.000200466,0.0000492469,0.00004187558,0.002815327],"genre_scores_gemma":[0.9683803,0.000009012935,0.02860935,0.0003302529,0.0002469443,0.000002955736,0.0004625564,0.00001484596,0.001943748],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3629046,"threshold_uncertainty_score":0.5888677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006045611241174022,"score_gpt":0.2217655190438631,"score_spread":0.215719907802689,"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."}}