{"id":"W2915402043","doi":"10.29242/stats.2013-2014","title":"ARL Statistics 2013–2014","year":2015,"lang":"en","type":"book","venue":"ARL statistics","topic":"Library Collection Development and Digital Resources","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Staffing; Fiscal year; Service (business); Library science; Statistics; Computer science; Political science; Business; Mathematics; Marketing; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.004864879,0.0008924455,0.0008857403,0.009008174,0.0008586494,0.00438164,0.002273307,0.001052096,0.08229694],"category_scores_gemma":[0.03806298,0.0006626333,0.0008156426,0.01975676,0.0004296234,0.002664449,0.001384088,0.002881204,0.1063203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007803843,"about_ca_system_score_gemma":0.01614157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.136001,"about_ca_topic_score_gemma":0.08193273,"domain_scores_codex":[0.9918211,0.0008910287,0.0008981324,0.0006482978,0.005078215,0.0006633573],"domain_scores_gemma":[0.9681937,0.005002015,0.002672035,0.001540913,0.02162443,0.0009669109],"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":[0.00001329187,0.000005819783,0.0003746658,0.00005784936,0.000003595525,0.000004301966,0.00001020855,0.0001050146,0.000006427216,0.001325761,0.9881945,0.009898543],"study_design_scores_gemma":[0.00001144886,0.000008789518,0.004279398,0.0002447745,0.000007378161,0.00002087932,0.00003369823,0.0002208144,0.00006706629,0.0007382486,0.9943474,0.00002014346],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0008368885,0.002666225,0.002555905,0.004282251,0.002292718,0.0002040751,0.9007647,0.002869945,0.08352724],"genre_scores_gemma":[0.005284338,0.005097074,0.002886345,0.002564765,0.001132221,0.0008580587,0.8593975,0.001554969,0.1212248],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9909918,"threshold_uncertainty_score":0.2753107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01983688192469913,"score_gpt":0.2231409913150374,"score_spread":0.2033041093903383,"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."}}