{"id":"W2298521947","doi":"10.18438/b8g62x","title":"Using EBLIP to Prepare Future Information Professionals","year":2016,"lang":"en","type":"article","venue":"Evidence Based Library and Information Practice","topic":"Library Science and Information Literacy","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Data science; World Wide Web; Library 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":["scholarly_communication","insufficient_payload"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.00119093,0.0001390369,0.0001177071,0.0003896583,0.000792441,0.001088809,0.0003626117,0.0001197521,0.001260934],"category_scores_gemma":[0.002724557,0.00009668534,0.00003998064,0.0009931617,0.0001052008,0.8677571,0.0001239106,0.00009832375,0.0004382383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003331447,"about_ca_system_score_gemma":0.0008665629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001377155,"about_ca_topic_score_gemma":5.179426e-8,"domain_scores_codex":[0.9979376,0.0003404566,0.0006088339,0.0001220063,0.0006823516,0.0003088055],"domain_scores_gemma":[0.9977388,0.001009665,0.0004716528,0.0002443233,0.0001997153,0.0003358285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003086705,0.00002347379,0.0008046372,0.00006834733,0.000007136883,5.731027e-7,0.009637643,0.00006317019,0.00003438169,0.8699482,0.03543193,0.08367179],"study_design_scores_gemma":[0.0002141337,0.0000600981,0.001850971,0.0003760849,0.000006635883,0.00000368595,0.009129371,0.0003754991,0.0003738089,0.0001708574,0.9872521,0.0001867989],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.01537719,0.000166285,0.0190088,0.919749,0.001342652,0.001379219,0.00007487618,0.0004610346,0.04244094],"genre_scores_gemma":[0.1136403,0.001192897,0.03644027,0.8458626,0.0009533033,0.00009420686,0.00008219078,0.00001167207,0.001722475],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.9518201,"threshold_uncertainty_score":0.9999481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02537940704575042,"score_gpt":0.3351125568060599,"score_spread":0.3097331497603095,"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."}}