{"id":"W2058684638","doi":"10.1080/01639370802322853","title":"Uniform Titles From AACR to RDA","year":2008,"lang":"en","type":"article","venue":"Cataloging & Classification Quarterly","topic":"Library Science and Information Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Rural Development Administration","keywords":"Cataloging; Consistency (knowledge bases); Computer science; Resource Description and Access; Point (geometry); Information retrieval; Duty; Resource (disambiguation); Authority control; Collocation (remote sensing); Library science; World Wide Web; Mathematics; Political science; Control (management); Law; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004026159,0.0004412529,0.0005617726,0.008506709,0.002773781,0.008789621,0.0014912,0.001273411,0.091377],"category_scores_gemma":[0.02353297,0.0003192005,0.0002544932,0.01627868,0.002655592,0.006320097,0.003692015,0.001936697,0.04775638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006354709,"about_ca_system_score_gemma":0.004067481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02212755,"about_ca_topic_score_gemma":0.01655754,"domain_scores_codex":[0.9914,0.002466006,0.001422914,0.0007904411,0.003315084,0.0006056299],"domain_scores_gemma":[0.9814518,0.003825303,0.0013572,0.005167805,0.007290466,0.0009074356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005596967,0.00003458526,0.001042966,0.0005008217,0.000004268745,0.0002038577,0.002598359,0.0002047314,0.001012886,0.2638721,0.5500178,0.1804516],"study_design_scores_gemma":[0.000002100845,0.000006316644,0.0008611355,0.0001846212,0.000001161155,0.00007011589,0.000285339,0.0000655542,0.0001599967,0.002831894,0.9955221,0.000009693288],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.009026717,0.006975249,0.01253138,0.007208015,0.007497154,0.0002420614,0.01219107,0.002586947,0.9417415],"genre_scores_gemma":[0.2295497,0.01067372,0.02870126,0.006422134,0.004018087,0.0008571697,0.02486919,0.002825704,0.6920831],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.091377,"threshold_uncertainty_score":0.3056865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04638663552312568,"score_gpt":0.2383307257378582,"score_spread":0.1919440902147325,"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."}}