{"id":"W4377019116","doi":"10.1101/2023.05.16.541039","title":"Ranked Subtree Prune and Regraft","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Royal Society Te Apārangi","keywords":"Phylogenetic tree; Tree (set theory); Ranking (information retrieval); Inference; Markov chain Monte Carlo; Search tree; Sequence (biology); Computer science; Markov chain; Bayesian probability; Mathematics; Theoretical computer science; Combinatorics; Algorithm; Biology; Artificial intelligence; Machine learning; Search algorithm; 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":[0.001526629,0.0005831632,0.00103412,0.001384017,0.000765285,0.001650406,0.001552459,0.0009338804,0.004456661],"category_scores_gemma":[0.008523238,0.0002930913,0.001024538,0.001386434,0.001272375,0.002184009,0.001744415,0.001306979,0.001474557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006890972,"about_ca_system_score_gemma":0.001235936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001779458,"about_ca_topic_score_gemma":0.002709898,"domain_scores_codex":[0.9982235,0.000386886,0.0001393344,0.0004018997,0.0006106298,0.0002378361],"domain_scores_gemma":[0.99538,0.00191553,0.0005332035,0.001301927,0.0005605121,0.000308679],"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":[0.001079867,0.0003591111,0.006371719,0.0004491282,0.00009811373,0.0004900034,0.0007942156,0.2376085,0.03568684,0.1334891,0.01175674,0.5718167],"study_design_scores_gemma":[0.00006712174,0.0004079136,0.001285407,0.00005730095,0.00003511514,0.0004861224,0.0002834701,0.8454428,0.01877579,0.1220811,0.01103882,0.00003903742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1394879,0.0004692123,0.8510227,0.0004513701,0.0001401435,0.0001908384,0.0006086913,0.002766618,0.004862572],"genre_scores_gemma":[0.3594333,0.0001644647,0.6345026,0.0002678179,0.00007135971,0.0001369474,0.001439701,0.0004847058,0.003499135],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004456661,"threshold_uncertainty_score":0.01490903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01643698223011236,"score_gpt":0.2175178473194202,"score_spread":0.2010808650893078,"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."}}