{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008770548,0.0003364769,0.0004646664,0.001236791,0.001809371,0.003361091,0.001021191,0.002655265,0.2702171],"category_scores_gemma":[0.04336148,0.000264668,0.0006334906,0.0009260936,0.0005759963,0.004839626,0.004118041,0.002659909,0.07603933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002013227,"about_ca_system_score_gemma":0.007950343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001421334,"about_ca_topic_score_gemma":0.005165155,"domain_scores_codex":[0.9971908,0.0009998822,0.0003479495,0.000175244,0.0009561655,0.0003300735],"domain_scores_gemma":[0.9765378,0.008299069,0.001256793,0.001688399,0.00790355,0.004314375],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004565791,0.0007312651,0.005097034,0.002976407,0.0000242327,0.0004794683,0.001831725,0.0001059336,0.001388686,0.00493963,0.4430431,0.5389259],"study_design_scores_gemma":[0.0001930331,0.0005825923,0.01222812,0.006978198,0.00008427494,0.0004372037,0.00417973,0.0002368777,0.003695879,0.005212678,0.9661195,0.00005195144],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.03292512,0.01140022,0.01822214,0.2345316,0.02732024,0.003372709,0.007942556,0.001842891,0.6624424],"genre_scores_gemma":[0.1362263,0.01550095,0.04230907,0.05421528,0.004526646,0.004414472,0.007361626,0.0004670126,0.7349786],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.2702171,"threshold_uncertainty_score":0.9039663,"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."}}