{"id":"W2770448539","doi":"10.1177/0961000617742465","title":"Data science in data librarianship: Core competencies of a data librarian","year":2017,"lang":"en","type":"article","venue":"Journal of Librarianship and Information Science","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":92,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Data curation; Computer science; Data management; Metadata; Data science; World Wide Web; Library science; Database","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0480827,0.0005371724,0.00124927,0.00472703,0.01226091,0.0284711,0.002789859,0.008559946,0.007072372],"category_scores_gemma":[0.08070689,0.001047943,0.0007229521,0.009131702,0.0205679,0.02887227,0.0256584,0.01198635,0.007233685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005100036,"about_ca_system_score_gemma":0.03375873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004621544,"about_ca_topic_score_gemma":0.003770939,"domain_scores_codex":[0.9359095,0.03786366,0.005852577,0.003443873,0.01203462,0.004895818],"domain_scores_gemma":[0.8738648,0.06595158,0.004886014,0.008735085,0.01924898,0.02731357],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"qualitative","study_design_scores_codex":[0.00006441931,0.00070959,0.01414045,0.001569982,0.00005534058,0.001246309,0.1741463,0.000505342,0.001252518,0.2561321,0.294525,0.2556525],"study_design_scores_gemma":[0.00002594694,0.00007928037,0.003065265,0.001148772,0.0000138491,0.0011993,0.05170782,0.0005840274,0.0005136337,0.09641507,0.845145,0.00010199],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.0298327,0.02254229,0.05904778,0.7451993,0.005257958,0.0005316462,0.0004183811,0.000792033,0.136378],"genre_scores_gemma":[0.4395953,0.03912186,0.1766742,0.25802,0.006410224,0.001076718,0.001130732,0.0005585655,0.07741246],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9715289,"threshold_uncertainty_score":0.2542886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6056634588857582,"score_gpt":0.4724691396796581,"score_spread":0.1331943192061001,"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."}}