{"id":"W2791337913","doi":"","title":"The Data Librarian's Handbook by Robin Rice and John Southall (review)","year":2017,"lang":"en","type":"article","venue":"Canadian journal of information science","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science; Sociology; History; Data science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.005906568,0.00006489336,0.0001199868,0.0002181727,0.002587261,0.003449883,0.008396667,0.00003382989,0.00003165754],"category_scores_gemma":[0.01221489,0.00003645762,0.0000212691,0.0004240219,0.001389563,0.01176561,0.0004443924,0.0001687376,0.0000847556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003247477,"about_ca_system_score_gemma":0.00145386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005402066,"about_ca_topic_score_gemma":0.001014016,"domain_scores_codex":[0.9983218,0.00001835928,0.0006017023,0.0001251277,0.0007037377,0.0002292785],"domain_scores_gemma":[0.9959903,0.0002078579,0.001079201,0.001791171,0.0005446883,0.0003867688],"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":[0.000001038846,0.000001067319,0.0004132577,0.000003837826,0.000002214302,9.343893e-7,0.0001066224,0.000001300197,0.00004584886,0.005357909,0.7020526,0.2920133],"study_design_scores_gemma":[0.00008042666,0.00001313024,0.004727478,0.00006726583,0.000004207627,0.00005814643,0.000432294,0.0004863271,0.000123097,0.002831657,0.9911154,0.00006056751],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.03198599,0.1200997,0.1280502,0.5794818,0.005247959,0.002379482,0.005129025,0.00009629135,0.1275295],"genre_scores_gemma":[0.9535687,0.02675376,0.01013387,0.008266175,0.0001386493,0.00001128101,0.0000351097,0.000009357148,0.001083114],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9215827,"threshold_uncertainty_score":0.9987112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.189351634746592,"score_gpt":0.3617327287424731,"score_spread":0.1723810939958811,"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."}}