{"id":"W2045434965","doi":"10.1300/j104v37n03_11","title":"Multilingual Subject Access: The Linking Approach of MACS","year":2004,"lang":"en","type":"article","venue":"Cataloging & Classification Quarterly","topic":"Library Science and Information Systems","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bibliothèque et Archives nationales du Québec","funders":"","keywords":"Subject access; Subject (documents); Computer science; German; World Wide Web; Thesaurus; Controlled vocabulary; Interface (matter); Information retrieval; Heading (navigation); Library science; Linguistics; Natural language processing; 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":[0.007861525,0.0005028958,0.0005655152,0.01213549,0.002144649,0.01155099,0.001737828,0.001113417,0.02233114],"category_scores_gemma":[0.02390337,0.0006577038,0.0007199328,0.009236739,0.003303028,0.01990354,0.008010679,0.001462211,0.004782895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0021899,"about_ca_system_score_gemma":0.0025163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002906292,"about_ca_topic_score_gemma":0.00253255,"domain_scores_codex":[0.9917052,0.004736645,0.0006569066,0.0008062436,0.001899157,0.0001957923],"domain_scores_gemma":[0.9858844,0.007013557,0.0007715012,0.003272674,0.002678305,0.0003796399],"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":[0.00007810102,0.0000626179,0.001937293,0.0004069764,0.00005860776,0.0003263608,0.01152486,0.001596867,0.001736874,0.6898338,0.02401271,0.268425],"study_design_scores_gemma":[0.00002387689,0.00004931376,0.001526291,0.0006506527,0.00008561093,0.0005660224,0.005700775,0.01423453,0.00337996,0.2745299,0.6991882,0.00006487644],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03447915,0.002611484,0.5663835,0.007833508,0.0007275236,0.0005607858,0.001607682,0.007378558,0.3784178],"genre_scores_gemma":[0.3943898,0.0028757,0.5314819,0.001766179,0.0008748471,0.000932997,0.004556402,0.003019436,0.06010268],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02233114,"threshold_uncertainty_score":0.07470512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05395550856753993,"score_gpt":0.2818520319610393,"score_spread":0.2278965233934993,"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."}}