{"id":"W2166603052","doi":"10.1093/bioinformatics/btl513","title":"THOR: targeted high-throughput ortholog reconstructor","year":2006,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency","funders":"Michael Smith Health Research BC; Genome Canada","keywords":"Sequence (biology); Genome; Computational biology; Throughput; Biology; Set (abstract data type); Whole genome sequencing; Computer science; Genetics; Gene","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":[0.00009748796,0.0001676129,0.0001566603,0.00003407308,0.0001108694,0.00002858598,0.0001562034,0.0001331368,0.00002243384],"category_scores_gemma":[0.00002621661,0.0001492944,0.00007873777,0.00006847703,0.0001061695,0.000001584004,0.0001237273,0.00005443256,0.0000519945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001046724,"about_ca_system_score_gemma":0.00004899303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003647073,"about_ca_topic_score_gemma":0.00003933179,"domain_scores_codex":[0.9991323,0.00001498141,0.0003408206,0.0001541444,0.0000926909,0.000265021],"domain_scores_gemma":[0.999429,0.000008477493,0.0001239558,0.0003230439,0.00007153302,0.00004393833],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001687243,0.000256822,0.08179639,0.0002293804,0.0004842193,0.00001256686,0.0005116385,0.001005039,0.7199848,0.01105939,0.1399201,0.04457092],"study_design_scores_gemma":[0.002559959,0.001079849,0.1416584,0.00002787081,0.0001180396,0.0001632198,0.0006946838,0.0009280509,0.288048,0.00406538,0.5590311,0.001625398],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9865454,0.0006258707,0.001499164,0.0000871156,0.0004401523,0.000166316,0.0001023362,0.00001365558,0.01052002],"genre_scores_gemma":[0.9563743,0.0002008083,0.04188213,0.0002321837,0.0003447506,0.00001227996,0.0001928498,0.00001745958,0.0007431734],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4319368,"threshold_uncertainty_score":0.608805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007001337011837258,"score_gpt":0.206458334003868,"score_spread":0.1994569969920307,"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."}}