{"id":"W2807099876","doi":"10.1007/978-1-4939-7804-5_16","title":"Manual Gene Curation and Functional Annotation","year":2018,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Génome Québec; Genome Canada","keywords":"Annotation; Sequence (biology); Computer science; Computational biology; Gene prediction; Set (abstract data type); Gene Annotation; Similarity (geometry); Genome project; Gene; Genome; Data mining; Biology; Artificial intelligence; Genetics; Image (mathematics); Programming language","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":[0.005943728,0.003281626,0.003877039,0.0138893,0.004277402,0.003633695,0.003749211,0.001608709,0.04235827],"category_scores_gemma":[0.01599527,0.001364543,0.003925811,0.009127233,0.001375273,0.001963667,0.003938429,0.004489868,0.04640797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001079509,"about_ca_system_score_gemma":0.006861454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005106302,"about_ca_topic_score_gemma":0.006591792,"domain_scores_codex":[0.9951172,0.0006304322,0.0008339653,0.001390448,0.00157989,0.0004480742],"domain_scores_gemma":[0.9863465,0.003122371,0.0007068355,0.004048683,0.005341057,0.0004346336],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001537933,0.0003962347,0.004732351,0.009057356,0.00049051,0.001309089,0.001985382,0.001299658,0.3109102,0.007523786,0.3459894,0.3147681],"study_design_scores_gemma":[0.0002135376,0.0001783483,0.01285026,0.0008456706,0.0006452024,0.001846315,0.0005801411,0.006697769,0.1076234,0.01289454,0.8553077,0.0003170458],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03591361,0.006177774,0.4822778,0.002236853,0.002536742,0.005158358,0.3323319,0.09366921,0.03969782],"genre_scores_gemma":[0.02724829,0.002276679,0.5365121,0.0009870838,0.0002966777,0.004736588,0.3912173,0.0145245,0.02220069],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04235827,"threshold_uncertainty_score":0.1417025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02275600658943236,"score_gpt":0.3700812001206779,"score_spread":0.3473251935312455,"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."}}