{"id":"W6950504354","doi":"10.5683/sp3/ce5sum","title":"Competencies for Data Librarianship","year":2009,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ontario Institute of Technology","funders":"","keywords":"Training (meteorology); Data collection; Occupational training; Competence (human resources)","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003099638,0.0005832287,0.0004116938,0.005036892,0.0010567,0.002604358,0.001523756,0.0008477669,0.02420474],"category_scores_gemma":[0.02619392,0.0004024593,0.0008090051,0.00780754,0.0003485984,0.003209714,0.003124603,0.002002803,0.01384698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00377111,"about_ca_system_score_gemma":0.00618074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09164607,"about_ca_topic_score_gemma":0.1651002,"domain_scores_codex":[0.9972873,0.0004912861,0.000408126,0.0004688517,0.0007967716,0.000547645],"domain_scores_gemma":[0.9857087,0.002667963,0.001834717,0.001636647,0.00624528,0.001906615],"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":[0.0001262687,0.00009068326,0.1039823,0.0006939261,0.00003556308,0.00003235867,0.0003596646,0.0003863649,0.00008355927,0.005080297,0.8491555,0.03997349],"study_design_scores_gemma":[0.00007152096,0.00003411113,0.2557435,0.0007600219,0.0000369899,0.0001477042,0.001552509,0.0007797642,0.0005152029,0.003178352,0.7371463,0.00003402691],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01676942,0.0006416237,0.001185511,0.003169839,0.0001224592,0.0001662831,0.9489104,0.0003807656,0.02865375],"genre_scores_gemma":[0.05971375,0.0005487449,0.006634399,0.0006299241,0.0000424221,0.0006773517,0.924166,0.000139071,0.007448383],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9973956,"threshold_uncertainty_score":0.1822253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1231416561436731,"score_gpt":0.3215455789031464,"score_spread":0.1984039227594733,"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."}}