{"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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":["scholarly_communication","insufficient_payload"],"category_scores_codex":[0.001186919,0.0001577953,0.0001445549,0.0002220837,0.000598122,0.001666087,0.0004298573,0.0001171206,0.002429233],"category_scores_gemma":[0.004024453,0.0001426106,0.00002459174,0.0006922897,0.0003208783,0.7363251,0.0001605729,0.0002080845,0.00135531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003920164,"about_ca_system_score_gemma":0.001251101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005678931,"about_ca_topic_score_gemma":1.893619e-7,"domain_scores_codex":[0.9977397,0.0004755701,0.0005413128,0.0001741335,0.0007352808,0.0003340231],"domain_scores_gemma":[0.9969935,0.001569499,0.0004550586,0.0002862653,0.000166431,0.000529274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002287944,0.00001852435,0.0008168198,0.00003182505,0.000004985779,0.000003117008,0.004238177,0.00005888639,0.000001391368,0.9187136,0.06849936,0.007384494],"study_design_scores_gemma":[0.0003227401,0.0001149415,0.001827002,0.00009684226,0.00001321435,0.000007151373,0.007107717,0.001799362,0.00005838378,0.001528898,0.9869092,0.0002145295],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"commentary","genre_scores_codex":[0.004814138,0.0012213,0.01897469,0.3171305,0.00117907,0.001148322,0.0002464031,0.0008348804,0.6544507],"genre_scores_gemma":[0.137004,0.003735501,0.06849455,0.7653205,0.0006590574,0.0001096621,0.0006672479,0.00003326069,0.02397615],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.9184099,"threshold_uncertainty_score":0.9994223,"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."}}