{"id":"W2205232092","doi":"10.18438/b8x01h","title":"A Holistic Look at Reference Statistics: Whither Librarians?","year":2015,"lang":"en","type":"article","venue":"Evidence Based Library and Information Practice","topic":"Library Science and Information Literacy","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Staffing; Computer science; Database transaction; Set (abstract data type); Reference desk; Summary statistics; Transaction data; Library science; Data science; World Wide Web; Statistics; Database; Political science; Mathematics","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":[0.0354614,0.0004249557,0.0009079631,0.009631923,0.004762486,0.01809419,0.001967575,0.00194275,0.009338913],"category_scores_gemma":[0.1148229,0.0007456149,0.0004415954,0.01771068,0.00717486,0.03870965,0.009560235,0.004751491,0.002588813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004451563,"about_ca_system_score_gemma":0.005626451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008082556,"about_ca_topic_score_gemma":0.01606275,"domain_scores_codex":[0.9708216,0.01761685,0.002103794,0.002081536,0.006341264,0.001035016],"domain_scores_gemma":[0.9103873,0.03812174,0.007532155,0.01469632,0.02411287,0.005149612],"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":[0.0001098379,0.0001568472,0.1184223,0.0006618695,0.0001387529,0.0002731399,0.1163629,0.0005754383,0.001983418,0.05731684,0.1068553,0.5971434],"study_design_scores_gemma":[0.00002311881,0.0003809883,0.1044799,0.002277111,0.0001091072,0.0007537471,0.2882982,0.002052794,0.004244537,0.1175281,0.4795847,0.0002676442],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.3441493,0.02499678,0.1100368,0.4302638,0.003130816,0.0001784901,0.002679619,0.002251981,0.08231235],"genre_scores_gemma":[0.8521221,0.008724743,0.09506874,0.02719063,0.001932788,0.0002901599,0.001863019,0.0008419234,0.01196591],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0354614,"threshold_uncertainty_score":0.1875401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06163160851868871,"score_gpt":0.321594089613701,"score_spread":0.2599624810950123,"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."}}