{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006662832,0.001999105,0.00130888,0.005571106,0.0008920387,0.007391911,0.002550521,0.00311012,0.01416442],"category_scores_gemma":[0.02699768,0.0007789856,0.001235585,0.005268853,0.002330008,0.01320118,0.003467264,0.003774364,0.01622372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002303139,"about_ca_system_score_gemma":0.002302473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003769408,"about_ca_topic_score_gemma":0.003901193,"domain_scores_codex":[0.9953383,0.002071388,0.0002956056,0.0006825922,0.001438853,0.0001731441],"domain_scores_gemma":[0.9788673,0.01472194,0.00084595,0.002350632,0.002570484,0.0006437119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007398201,0.0002140834,0.005815912,0.001694084,0.0001353557,0.0001112964,0.0004199641,0.01338387,0.0006563817,0.07615688,0.08232415,0.8190141],"study_design_scores_gemma":[0.00003389316,0.0001851621,0.003166459,0.00193486,0.00007134215,0.0002025934,0.0008301471,0.07389855,0.001610834,0.3561926,0.5617191,0.0001545157],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"commentary","genre_scores_codex":[0.01464923,0.0935053,0.6196736,0.1352417,0.004000296,0.0003735452,0.003269901,0.01016554,0.1191209],"genre_scores_gemma":[0.2528259,0.1402563,0.5320098,0.01447862,0.01318474,0.0005545191,0.006745434,0.001943403,0.03800128],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.01416442,"threshold_uncertainty_score":0.04738468,"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."}}