{"id":"W2127143129","doi":"10.1093/bioinformatics/btl116","title":"PseudoPipe: an automated pseudogene identification pipeline","year":2006,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":212,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Toronto","funders":"National Institutes of Health; National Human Genome Research Institute; University of Toronto","keywords":"Pseudogene; Genome; Biology; Genetics; Homology (biology); Computational biology; Gene; Retrotransposon; Intergenic region; Transposable element","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":[0.0001556221,0.0001477875,0.0001138086,0.00004409842,0.0001178176,0.00005133017,0.0001812496,0.000104744,0.000006806094],"category_scores_gemma":[0.00001987937,0.0001399938,0.00005536991,0.00008674458,0.00005305514,0.000002954614,0.00007061284,0.00003415492,0.00005002201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001068609,"about_ca_system_score_gemma":0.00003806048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002850582,"about_ca_topic_score_gemma":0.00003351271,"domain_scores_codex":[0.9990903,0.00001710586,0.0004077455,0.0001561735,0.0001169045,0.0002117905],"domain_scores_gemma":[0.9992893,0.000004095204,0.0001427426,0.0003951242,0.0001152738,0.00005349698],"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":[0.0000299968,0.0001481926,0.003517008,0.00004895831,0.00004154873,0.000001078716,0.0001313697,0.00163552,0.9577105,0.0005492687,0.03049935,0.005687203],"study_design_scores_gemma":[0.001849446,0.0005118757,0.09652913,0.00001773098,0.0001099315,0.0000947186,0.0005124513,0.21108,0.5692639,0.0007318874,0.1180878,0.001211177],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9891344,0.0004069936,0.006575117,0.00007423107,0.0002465698,0.0002012739,0.00006747516,0.00005729922,0.003236585],"genre_scores_gemma":[0.9881338,0.0001051439,0.01006067,0.0001469232,0.0003408122,0.00001488946,0.0004903535,0.00001820414,0.0006892327],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3884466,"threshold_uncertainty_score":0.570878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01039699376225014,"score_gpt":0.2506661242629637,"score_spread":0.2402691305007136,"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."}}