{"id":"W2802730037","doi":"10.1186/s12864-018-4462-y","title":"Resolution effects in reconstructing ancestral genomes","year":2018,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Genome Rearrangement Algorithms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Synteny; Genome; Biology; Evolutionary biology; Computational biology; Comparative genomics; Resolution (logic); Genomics; DNA microarray; Genetics; Gene; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01304657,0.0006549684,0.0005989476,0.001453036,0.0008601705,0.001515624,0.001156482,0.001377938,0.001400999],"category_scores_gemma":[0.06307229,0.0007572907,0.0008774017,0.001552253,0.001980107,0.001765682,0.002493555,0.002304858,0.0002633104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009513057,"about_ca_system_score_gemma":0.0004255875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009711315,"about_ca_topic_score_gemma":0.001096739,"domain_scores_codex":[0.995466,0.002734883,0.0002895541,0.0005288863,0.0008050583,0.000175562],"domain_scores_gemma":[0.940535,0.05269361,0.00199813,0.002998997,0.001301589,0.0004726624],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00270364,0.0001939305,0.08015722,0.0009737017,0.000591942,0.001037653,0.001506929,0.7126663,0.09049646,0.02365883,0.000790489,0.08522296],"study_design_scores_gemma":[0.0001907415,0.0005933102,0.03899883,0.0001731903,0.0003982546,0.002092819,0.0004238052,0.7872793,0.1236421,0.04240483,0.003653508,0.0001492951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.744774,0.001351831,0.2501524,0.000583037,0.00004889231,0.00004274467,0.0002205373,0.0009051294,0.001921334],"genre_scores_gemma":[0.8384416,0.0003041371,0.1599918,0.0001626513,0.0000371401,0.00003805488,0.0003733314,0.0004065881,0.0002446989],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01304657,"threshold_uncertainty_score":0.06899762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01774716360236049,"score_gpt":0.2492426251077188,"score_spread":0.2314954615053583,"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."}}