{"id":"W4288060363","doi":"10.18357/kula.229","title":"Using Linked Data Sources to Enhance Catalog Discovery","year":2022,"lang":"en","type":"article","venue":"KULA knowledge creation dissemination and preservation studies","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Andrew W. Mellon Foundation","keywords":"Metadata; Cataloging; Computer science; Usability; Library catalog; World Wide Web; Data science; Data curation; Linked data; Data discovery; Information retrieval; Semantic Web; 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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.06269934,0.0007011426,0.0009990047,0.01131094,0.004061129,0.01326805,0.003546552,0.001981738,0.004900644],"category_scores_gemma":[0.2002583,0.001284251,0.0009940123,0.01299321,0.002231312,0.03064369,0.01302002,0.002431487,0.001540309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002595244,"about_ca_system_score_gemma":0.00609586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005602017,"about_ca_topic_score_gemma":0.006741642,"domain_scores_codex":[0.9424703,0.03665905,0.00414875,0.003326549,0.01251105,0.0008842279],"domain_scores_gemma":[0.6240723,0.2844032,0.008886796,0.05908214,0.02134207,0.00221348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001792888,0.00400599,0.04290346,0.003374916,0.0004811958,0.0006983349,0.04525208,0.007882516,0.02407411,0.08663616,0.01027413,0.7726242],"study_design_scores_gemma":[0.002120499,0.003360835,0.03636273,0.002119894,0.001270342,0.001448885,0.02927623,0.1716584,0.1376468,0.2747667,0.3387392,0.001229576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2555186,0.001278835,0.6840195,0.007716354,0.0002328599,0.004070293,0.002151498,0.01353143,0.03148075],"genre_scores_gemma":[0.3090139,0.000507794,0.6824282,0.0006326077,0.00008097413,0.001008792,0.00191911,0.0005698345,0.003838823],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9867319,"threshold_uncertainty_score":0.3315897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1447546835511525,"score_gpt":0.4343003403280847,"score_spread":0.2895456567769323,"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."}}