{"id":"W2152386885","doi":"10.1186/1471-2164-15-s6-s5","title":"Gene order alignment on trees with multiOrthoAlign","year":2014,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Genome Rearrangement Algorithms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Phylogenetic tree; Heuristic; Dynamic programming; Biology; Genome; Order (exchange); Tree (set theory); Phylogenetics; DNA microarray; Phylogenetic network; Multiple sequence alignment; Computational biology; Computer science; Gene; Artificial intelligence; Algorithm; Genetics; Sequence alignment; Mathematics; Combinatorics","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":[0.0001467987,0.0001824511,0.0001271295,0.00003602155,0.00006403183,0.00002489036,0.0001720516,0.00008738162,0.00001954168],"category_scores_gemma":[0.00001949288,0.0001558995,0.00005177165,0.00005431803,0.00004089556,0.000001399577,0.00006456417,0.00004349826,0.00008055336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002907495,"about_ca_system_score_gemma":0.00005373477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001124639,"about_ca_topic_score_gemma":0.0001407822,"domain_scores_codex":[0.9990417,0.00004679401,0.0001449207,0.0003737109,0.0001360993,0.0002567753],"domain_scores_gemma":[0.999308,0.000009174789,0.0000670013,0.0004755081,0.00005416018,0.00008609085],"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":[0.0002068491,0.0001812641,0.009393071,0.00001870967,0.0001188794,0.000002279837,0.0001050122,0.01085776,0.969789,0.000156337,0.0009187234,0.008252056],"study_design_scores_gemma":[0.002676664,0.001558478,0.01358883,0.00001078483,0.00006022576,0.00001815438,0.0001262323,0.001307983,0.7698759,0.00006763151,0.2100393,0.0006697967],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9669645,0.0001147146,0.03016477,0.00005919587,0.00009876359,0.000268931,0.00001312933,0.00001808646,0.002297948],"genre_scores_gemma":[0.9518206,0.00007323764,0.04548566,0.0003309338,0.0003777065,0.0000417684,0.0001500297,0.00005256043,0.001667479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2091206,"threshold_uncertainty_score":0.6357397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01155474417934116,"score_gpt":0.2193273690388745,"score_spread":0.2077726248595333,"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."}}