{"id":"W4284879781","doi":"10.29242/stats.2020","title":"ARL Statistics 2020","year":2022,"lang":"en","type":"book","venue":"ARL statistics","topic":"Library Collection Development and Digital Resources","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Staffing; Library science; Service (business); Statistics; Summary 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":[],"consensus_categories":[],"category_scores_codex":[0.004023936,0.001249239,0.001077887,0.008060108,0.001163183,0.006511268,0.001990628,0.001523529,0.14909],"category_scores_gemma":[0.02709646,0.0007257768,0.0008460477,0.0175097,0.0004318068,0.003482779,0.001285069,0.003157766,0.2731614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006051512,"about_ca_system_score_gemma":0.01409034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1055694,"about_ca_topic_score_gemma":0.07743125,"domain_scores_codex":[0.9933957,0.0008591225,0.0006052928,0.0004553684,0.004165179,0.0005193854],"domain_scores_gemma":[0.9776818,0.00356065,0.00142504,0.001026528,0.01543251,0.0008735227],"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":[0.00000932166,0.000003865912,0.0001284492,0.00004869542,0.000001626808,0.000004456993,0.000007799577,0.00005646041,0.000007028049,0.001233655,0.9883867,0.01011196],"study_design_scores_gemma":[0.000005467351,0.000004897514,0.0007157348,0.0001240159,0.000002390687,0.00001736946,0.00001695921,0.00008265401,0.00002562576,0.0005526054,0.9984431,0.000009313565],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0005237229,0.005725876,0.002914066,0.007366203,0.004589877,0.0002389544,0.6847917,0.005197412,0.2886522],"genre_scores_gemma":[0.003590767,0.008448232,0.003985191,0.004324241,0.001702095,0.0007502753,0.6117012,0.002670887,0.362827],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.14909,"threshold_uncertainty_score":0.4987558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01126059874215754,"score_gpt":0.20460421904937,"score_spread":0.1933436203072125,"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."}}