{"id":"W4404534860","doi":"10.29242/stats.2023","title":"ARL Statistics 2023","year":2024,"lang":"en","type":"book","venue":"ARL statistics","topic":"Library Science and Administration","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Staffing; Library science; Service (business); Statistics; Computer science; Business; Political science; 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":[0.004491499,0.001143245,0.001109897,0.006652091,0.001081488,0.005336623,0.001914268,0.001463859,0.1838348],"category_scores_gemma":[0.03629157,0.0007766208,0.0009501192,0.01610041,0.0004220687,0.002766041,0.001247546,0.00373985,0.2794382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005169948,"about_ca_system_score_gemma":0.01343116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1010282,"about_ca_topic_score_gemma":0.07597809,"domain_scores_codex":[0.9926253,0.00118929,0.0006394044,0.000456005,0.004593467,0.0004965199],"domain_scores_gemma":[0.9708138,0.006557637,0.00172957,0.001608115,0.01846787,0.0008229667],"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":[0.000007410591,0.000003257911,0.0001097266,0.00003518403,0.000001741343,0.000003882848,0.000006784263,0.00005111421,0.000005530521,0.001104292,0.989168,0.009502824],"study_design_scores_gemma":[0.000007222935,0.000004487355,0.00071442,0.0001405175,0.000002993303,0.00002082517,0.00001714941,0.0001223595,0.00002841546,0.0009525119,0.9979785,0.00001064508],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003959281,0.003867723,0.005163939,0.008538832,0.00429938,0.0003218426,0.693689,0.006214245,0.2775091],"genre_scores_gemma":[0.003650098,0.007379365,0.007183782,0.005109621,0.002132762,0.001083698,0.6109695,0.004154705,0.3583365],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9933479,"threshold_uncertainty_score":0.6149888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03680569226384769,"score_gpt":0.3283273912072769,"score_spread":0.2915216989434292,"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."}}