{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004445867,0.002523456,0.001813958,0.01697118,0.001684622,0.004349957,0.002077227,0.001757998,0.01792021],"category_scores_gemma":[0.01929558,0.001167452,0.002059816,0.01744902,0.0007752111,0.006832576,0.008079696,0.002300389,0.01542142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002009777,"about_ca_system_score_gemma":0.00342986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007340363,"about_ca_topic_score_gemma":0.01230243,"domain_scores_codex":[0.9958995,0.0007418558,0.0009215233,0.0009410179,0.00129183,0.0002043269],"domain_scores_gemma":[0.992713,0.002844601,0.0009070405,0.001621576,0.001061138,0.0008525799],"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":[0.001243686,0.0002065261,0.009852496,0.008305646,0.0005845839,0.000782254,0.001222853,0.00351443,0.01129352,0.03754713,0.6961545,0.2292922],"study_design_scores_gemma":[0.0002277042,0.00009149533,0.00775372,0.001088656,0.0002750185,0.0005089843,0.0003713861,0.008138916,0.00631328,0.03695074,0.9381099,0.0001701768],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.01126712,0.00991121,0.2181798,0.004207992,0.0009503448,0.001753398,0.5212914,0.2065123,0.02592641],"genre_scores_gemma":[0.0229555,0.00440382,0.34487,0.00139935,0.0002702727,0.001936164,0.6073374,0.01317207,0.003655375],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.01792021,"threshold_uncertainty_score":0.05994904,"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."}}