{"id":"W1534962401","doi":"10.1007/978-3-540-73545-8_8","title":"Integer Programming Formulations and Computations Solving Phylogenetic and Population Genetic Problems with Missing or Genotypic Data","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":67,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Missing data; Phylogenetic tree; Integer programming; Genotype; Genetic data; Integer (computer science); Computation; Genetic programming; Population; Mathematics; Mathematical optimization; Computer science; Biology; Statistics; Genetics; Algorithm; Artificial intelligence; Programming language; Demography; Sociology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004887966,0.0002693985,0.0002669445,0.0002231712,0.000308786,0.0001160914,0.0003256131,0.0002644745,0.000003652163],"category_scores_gemma":[0.0001351587,0.0002181698,0.0000204862,0.0001464394,0.0003733252,0.00001383643,0.0004899906,0.0002091979,5.665168e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004396931,"about_ca_system_score_gemma":0.0001912237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006077248,"about_ca_topic_score_gemma":0.001445312,"domain_scores_codex":[0.9981666,0.0000280622,0.0003709359,0.0008955401,0.0001809136,0.0003579165],"domain_scores_gemma":[0.9988834,0.0001437259,0.0002286553,0.0005171225,0.0001284765,0.00009865018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001791912,0.00001876237,0.04042179,0.00007188886,0.00003973255,0.000005890776,0.0002149953,0.05398462,0.000683032,0.00008639113,0.000004713149,0.9044502],"study_design_scores_gemma":[0.0008965548,0.0009088408,0.2727468,0.0005650357,0.0001409438,0.0004012538,0.000003502824,0.7048343,0.0001105307,0.0160947,0.002088533,0.001208986],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03097975,0.001505589,0.9667462,0.0001365412,0.00009613179,0.0004392172,0.00001176111,0.00001399552,0.00007086049],"genre_scores_gemma":[0.530726,0.0001054917,0.4685687,0.0001740621,0.0001735937,0.000005022811,0.0001661821,0.00002430778,0.00005668649],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9032413,"threshold_uncertainty_score":0.8896706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03867889263134464,"score_gpt":0.2933822906259842,"score_spread":0.2547033979946396,"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."}}