{"id":"W4399665511","doi":"10.18438/eblip30527","title":"Machine Learning Offers Opportunities to Advance Library Services","year":2024,"lang":"en","type":"article","venue":"Evidence Based Library and Information Practice","topic":"AI in Service Interactions","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Gradient boosting; Artificial intelligence; Random forest; Python (programming language); Machine learning; Boosting (machine learning); Natural language processing; Usability; Information retrieval; WordNet; Plug-in; World Wide Web; Programming language; Human–computer interaction","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"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.000268569,0.000177444,0.0001189427,0.0004057841,0.0002428983,0.003076841,0.0006622454,0.00005823004,0.000351222],"category_scores_gemma":[0.0002043245,0.0001666878,0.00004115781,0.0006793712,0.00002643434,0.570052,0.0004645383,0.00039939,0.000419421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001173289,"about_ca_system_score_gemma":0.0002411608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001006381,"about_ca_topic_score_gemma":6.464819e-8,"domain_scores_codex":[0.9986449,0.0002015813,0.0003959275,0.0002501986,0.0002972318,0.0002101988],"domain_scores_gemma":[0.9970442,0.002186914,0.0001626139,0.0003600273,0.00004208797,0.0002041438],"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":[0.0001764354,0.0000335906,0.0003529211,0.001003587,0.00004073341,0.00007073044,0.003133759,0.004145663,0.00007035545,0.8459223,0.005554529,0.1394954],"study_design_scores_gemma":[0.00004880164,0.0001136023,0.0002304676,0.0006820874,0.00001005269,0.00005416737,0.0007786675,0.20858,0.000717208,0.0002299001,0.7883875,0.0001675165],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.001607395,0.005563418,0.2271251,0.7144145,0.001410034,0.0005171417,0.00003523163,0.003100171,0.04622698],"genre_scores_gemma":[0.1834988,0.01309475,0.1963044,0.5999779,0.000338511,0.0001597564,0.0002215788,0.00005900858,0.006345311],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.8456924,"threshold_uncertainty_score":0.9979581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01843699531240544,"score_gpt":0.2644004169151561,"score_spread":0.2459634216027506,"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."}}