{"id":"W2143438047","doi":"10.1093/bioinformatics/btr224","title":"Mapping ancestral genomes with massive gene loss: A matrix sandwich problem","year":2011,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genome Rearrangement Algorithms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche","keywords":"Genome; Extant taxon; Biology; Gene; Computational biology; Heuristic; Gene duplication; Genetics; Evolutionary biology; Computer science; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.000115128,0.0002076957,0.0001611619,0.00007576431,0.00007806101,0.00003520415,0.0002444727,0.0001118452,0.00005184683],"category_scores_gemma":[0.000005489391,0.0001663054,0.00005955572,0.000142815,0.00008992968,0.00001505342,0.0001028419,0.00007481391,0.00007417513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001999272,"about_ca_system_score_gemma":0.00009162502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007953504,"about_ca_topic_score_gemma":0.000006306799,"domain_scores_codex":[0.9989671,0.00001307009,0.0003026802,0.0001742687,0.0001732742,0.0003695858],"domain_scores_gemma":[0.9992908,0.00000270132,0.0001742859,0.0003344698,0.00009346853,0.0001043181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001882833,0.001783213,0.3622558,0.005047314,0.005617403,0.0004225531,0.06463186,0.001447934,0.4001594,0.002164832,0.01706854,0.1375183],"study_design_scores_gemma":[0.01683009,0.008751523,0.0751034,0.0005506192,0.0005303253,0.001460392,0.02086022,0.009898743,0.5904527,0.001513834,0.266948,0.007100243],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9335054,0.001474315,0.04149363,0.00008038468,0.0001532178,0.001027874,0.00007391754,0.00008211629,0.02210913],"genre_scores_gemma":[0.5202143,0.0005174914,0.4773751,0.000136229,0.0002068498,0.00006410352,0.000208854,0.00004384142,0.001233301],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4358814,"threshold_uncertainty_score":0.6781735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01843788919145358,"score_gpt":0.2257237287311584,"score_spread":0.2072858395397048,"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."}}