{"id":"W2951333474","doi":"10.1186/s13326-016-0067-z","title":"FALDO: a semantic standard for describing the location of nucleotide and protein feature annotation","year":2016,"lang":"en","type":"article","venue":"Journal of Biomedical Semantics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro One (Canada)","funders":"National Bioscience Database Center; Basic Energy Sciences; Japan Science and Technology Agency; National Institutes of Health; Research Organization of Information and Systems; Swiss Institute of Bioinformatics; Staatssekretariat für Bildung, Forschung und Innovation; U.S. Department of Energy; Australian Government; Office of Science; Scottish Government","keywords":"SPARQL; UniProt; Computer science; Annotation; Ontology; Information retrieval; Feature (linguistics); Semantic Web; Computational biology; RDF; Biology; Artificial intelligence; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":false,"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":[0.008933141,0.001270687,0.0009482445,0.007533036,0.001991679,0.005561047,0.003270347,0.003156892,0.009122744],"category_scores_gemma":[0.01508309,0.001039413,0.001626088,0.005351725,0.002379358,0.01026643,0.005428114,0.003474313,0.008077729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003514982,"about_ca_system_score_gemma":0.005776625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01674253,"about_ca_topic_score_gemma":0.0152397,"domain_scores_codex":[0.9943084,0.0009500395,0.002032978,0.0006889983,0.001585562,0.0004340156],"domain_scores_gemma":[0.9872276,0.003075393,0.001216124,0.004481622,0.003392649,0.000606577],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009967574,0.0003381772,0.007567409,0.002246826,0.0001591369,0.0008645942,0.002634179,0.006854875,0.029615,0.5309153,0.2300666,0.1877411],"study_design_scores_gemma":[0.000124061,0.0001127135,0.002549045,0.0008398869,0.00007170877,0.0008437815,0.0007979987,0.01500728,0.01365371,0.1014521,0.8643513,0.0001963714],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008411813,0.0006596143,0.8400033,0.002484746,0.000538921,0.001062062,0.07972488,0.04425231,0.02286243],"genre_scores_gemma":[0.07292979,0.001721949,0.6509436,0.003899581,0.0002698446,0.00253007,0.2488448,0.007435905,0.01142433],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01674253,"threshold_uncertainty_score":0.04724354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02384984259902257,"score_gpt":0.2656073319421278,"score_spread":0.2417574893431052,"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."}}