{"id":"W1999357269","doi":"10.1186/1471-2148-13-274","title":"Inferring explicit weighted consensus networks to represent alternative evolutionary histories","year":2013,"lang":"en","type":"article","venue":"BMC Evolutionary Biology","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Reticulate evolution; Reticulate; Phylogenetic network; Phylogenetic tree; Biology; Inference; Phylogenetics; Evolutionary biology; Horizontal gene transfer; Tree (set theory); Gene; Artificial intelligence; Genetics; Computer science; Paleontology; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0000991302,0.0002601085,0.00024669,0.00008818932,0.0002335478,0.00001085489,0.0002688748,0.000176607,0.00008064908],"category_scores_gemma":[0.0001390363,0.0002495603,0.000118032,0.0001252006,0.0001939718,0.000001748972,0.0004820473,0.0001062028,0.00007858904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000969286,"about_ca_system_score_gemma":0.0001226412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006315206,"about_ca_topic_score_gemma":0.00008987122,"domain_scores_codex":[0.9982964,0.0001509391,0.0003567454,0.0006413232,0.0000877107,0.0004668662],"domain_scores_gemma":[0.9988973,0.00008128685,0.0001117325,0.0004647874,0.0002870334,0.000157932],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003198603,0.0001881047,0.6109977,0.00001954648,0.0003783109,0.000004810991,0.0002060084,0.009036931,0.2792469,0.01048387,0.08813819,0.0009797156],"study_design_scores_gemma":[0.001320767,0.001012483,0.6408005,0.00001906813,0.00004648097,0.0001168186,0.0003488642,0.01007579,0.0041779,0.008424262,0.3325422,0.001114877],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9756536,0.007590258,0.01172582,0.000822705,0.001078194,0.0006456028,0.00006289763,0.0000240856,0.002396832],"genre_scores_gemma":[0.9880271,0.0001823567,0.008960078,0.0003997738,0.0008599959,0.0003061869,0.0001612689,0.0000260143,0.00107722],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.275069,"threshold_uncertainty_score":0.9999956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01844260581446351,"score_gpt":0.2503981657109113,"score_spread":0.2319555598964478,"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."}}