{"id":"W4413874168","doi":"10.1007/978-3-031-94928-9_4","title":"Whole-Genome Duplication Detection with Phylogenomics Reconciliation: A Scalable Approach","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Chromosomal and Genetic Variations","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Phylogenomics; Computer science; Gene duplication; Scalability; Genome; Computational biology; Artificial intelligence; Biology; Phylogenetic tree; Genetics; Gene; Database","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":[0.003040018,0.003427284,0.004515135,0.004368635,0.001633952,0.004307829,0.006281672,0.002486923,0.008395716],"category_scores_gemma":[0.00809035,0.001912741,0.003259759,0.006864854,0.000858343,0.004133789,0.0057921,0.002592115,0.006145042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008884997,"about_ca_system_score_gemma":0.002475203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005169825,"about_ca_topic_score_gemma":0.00901655,"domain_scores_codex":[0.9977203,0.0004320198,0.0001516052,0.0009220651,0.0006012032,0.0001727566],"domain_scores_gemma":[0.9958164,0.001628884,0.0002494628,0.001588505,0.0005326102,0.0001841173],"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":[0.001427093,0.0005746653,0.004961402,0.001070877,0.001686936,0.0007006347,0.0004272566,0.05845358,0.05147135,0.006916153,0.05107082,0.8212393],"study_design_scores_gemma":[0.0004842898,0.0002001723,0.004041948,0.00007360292,0.0005915929,0.0006282639,0.0003961208,0.912782,0.01928383,0.04660969,0.01475966,0.0001488105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02158354,0.001515756,0.9088986,0.0007088023,0.000326354,0.0004233021,0.005703083,0.05798637,0.00285414],"genre_scores_gemma":[0.07013623,0.0004890101,0.9135507,0.000240046,0.00016884,0.000385248,0.01059596,0.002197723,0.002236262],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008395716,"threshold_uncertainty_score":0.02808642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01480185743604391,"score_gpt":0.1939503713312017,"score_spread":0.1791485138951578,"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."}}