{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001506347,0.001430457,0.0009454992,0.001363429,0.0006552506,0.001290749,0.001862759,0.001039069,0.02239572],"category_scores_gemma":[0.002541886,0.001182654,0.001712379,0.0009953172,0.0004794547,0.001097097,0.001556594,0.00174897,0.0120049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006044487,"about_ca_system_score_gemma":0.0009103054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001461006,"about_ca_topic_score_gemma":0.002148526,"domain_scores_codex":[0.9993267,0.0001060737,0.00005085878,0.000214161,0.0002336799,0.00006853764],"domain_scores_gemma":[0.9990163,0.0003741233,0.0001073537,0.0003079068,0.0001217683,0.00007260743],"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":[0.004315577,0.0003239739,0.00724988,0.002249874,0.0008191192,0.001142717,0.0006564732,0.02410252,0.2617278,0.01485888,0.2866061,0.3959471],"study_design_scores_gemma":[0.0008152536,0.0004210784,0.005657209,0.0001335632,0.0002409936,0.001922649,0.0001878664,0.4605955,0.3503755,0.01355533,0.1658232,0.0002717577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01566844,0.0002194321,0.5726398,0.0001275023,0.0001193363,0.0001952343,0.01291847,0.394901,0.003210684],"genre_scores_gemma":[0.128281,0.000392473,0.770943,0.0003183919,0.00006602165,0.0007344611,0.05686252,0.0320276,0.01037465],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02239572,"threshold_uncertainty_score":0.07492119,"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."}}