{"id":"W2607390459","doi":"10.1093/bioinformatics/btx213","title":"BioCIDER: a Contextualisation InDEx for biological Resources discovery","year":2017,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research","funders":"Biotechnology and Biological Sciences Research Council; Directorate for Biological Sciences; European Commission","keywords":"Contextualization; Documentation; Computer science; World Wide Web; Source code; Index (typography); Code (set theory); Open source; Software; Open source software; Data science; Programming language","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002437844,0.0001203916,0.0001420693,0.00002613207,0.0003430404,0.0001830193,0.0003590254,0.0002467379,0.000003689417],"category_scores_gemma":[0.001038339,0.00008723673,0.00009670163,0.00001540183,0.0003592742,0.0000133113,0.0001575917,0.0000527482,0.000008175301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007333829,"about_ca_system_score_gemma":0.00003335289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001208359,"about_ca_topic_score_gemma":0.00001453515,"domain_scores_codex":[0.9992988,0.00001421522,0.0002422441,0.0001397067,0.00009260461,0.0002125033],"domain_scores_gemma":[0.9991971,0.00003497729,0.0002202188,0.0004375752,0.00005175485,0.0000583711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001612493,0.0003437436,0.1368059,0.0004532717,0.0005387832,0.000006320976,0.00297145,0.0000237553,0.0427244,0.004911595,0.1432841,0.6663242],"study_design_scores_gemma":[0.002388488,0.001216689,0.05011538,0.0000605822,0.0000275251,0.00001968066,0.001961012,0.003794507,0.01380477,0.0009871438,0.9250529,0.0005713639],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8708698,0.0003793594,0.1233797,0.0009316151,0.0005853634,0.0004080571,0.0001435071,0.00005402266,0.003248639],"genre_scores_gemma":[0.987828,0.00009436996,0.01045283,0.0004387241,0.0003533119,0.00003496132,0.0001344597,0.00000853098,0.0006547972],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7817688,"threshold_uncertainty_score":0.3557411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04625547988553334,"score_gpt":0.3059128523529986,"score_spread":0.2596573724674653,"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."}}