{"id":"W2030916610","doi":"10.1080/01639374.2012.680835","title":"FRBR and Linked Data: Connecting FRBR and Linked Data","year":2012,"lang":"en","type":"article","venue":"Cataloging & Classification Quarterly","topic":"Library Science and Information Systems","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Linked data; World Wide Web; Computer science; Context (archaeology); Component (thermodynamics); Metadata; Semantic Web; Resource (disambiguation); Focus (optics); Cataloging; Information retrieval; Geography","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":[0.02678162,0.0009982992,0.001082084,0.01428875,0.002688007,0.02184705,0.003620598,0.005029759,0.009637506],"category_scores_gemma":[0.0633075,0.001291149,0.001786259,0.02200355,0.01273256,0.05158814,0.01787319,0.006099475,0.003384857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004391054,"about_ca_system_score_gemma":0.005808661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009538374,"about_ca_topic_score_gemma":0.005522727,"domain_scores_codex":[0.9689876,0.01918115,0.00210425,0.002394693,0.006293369,0.001038886],"domain_scores_gemma":[0.9499136,0.02874158,0.002866218,0.01194219,0.005307099,0.001229323],"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":[0.00002585235,0.00002725614,0.0008959214,0.0003727323,0.00003487228,0.0002250026,0.002602204,0.00111302,0.0002289052,0.9154397,0.007146925,0.07188752],"study_design_scores_gemma":[0.00001500242,0.00002704279,0.0006536455,0.0009921838,0.00003320729,0.0004112637,0.001957794,0.006243846,0.0004870755,0.7113625,0.2777538,0.00006266827],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004982209,0.01207163,0.8888816,0.02666159,0.001189004,0.0003719974,0.0007488899,0.00202705,0.06306607],"genre_scores_gemma":[0.1541192,0.02480404,0.778251,0.009297972,0.002568292,0.001035524,0.004323142,0.001565251,0.02403556],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02678162,"threshold_uncertainty_score":0.1416364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1455780634709184,"score_gpt":0.3059711345770734,"score_spread":0.160393071106155,"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."}}