{"id":"W1524343434","doi":"10.5860/lrts.52n2.29","title":"Subject Access Tools in English for Canadian Topics","year":2008,"lang":"en","type":"article","venue":"Library Resources and Technical Services","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Government of Canada","keywords":"Subject (documents); Cataloging; Subject access; Dewey Decimal Classification; Terminology; Library of congress; Computer science; Library science; Library of Congress Classification; Library classification; World Wide Web; Controlled vocabulary; Library catalog; Linguistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008260446,0.0001325139,0.0001752778,0.0001794764,0.0001703121,0.0005225487,0.001485208,0.0001566834,0.000009464749],"category_scores_gemma":[0.00002442913,0.000110486,0.00003955219,0.0004602483,0.00004543734,0.002508881,0.0004790213,0.0001827441,5.449389e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001430642,"about_ca_system_score_gemma":0.00005379408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004378208,"about_ca_topic_score_gemma":0.007071816,"domain_scores_codex":[0.9989985,0.0000239201,0.0001935772,0.0003553717,0.0001127013,0.0003159597],"domain_scores_gemma":[0.9993551,0.0001126378,0.00005149034,0.0002990766,0.00002248148,0.0001592447],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001716728,0.0002799283,0.4076981,0.001550873,0.00004923572,0.0007512149,0.01007125,0.00001340035,0.0007123545,0.2539878,0.01130294,0.3134113],"study_design_scores_gemma":[0.0009343282,0.0003427431,0.1090977,0.0003881622,0.00001078445,0.0001547499,0.0001012073,0.003494326,0.008030348,0.09920079,0.7770986,0.001146242],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9382542,0.01783491,0.005283521,0.01372729,0.0002852345,0.001561049,0.00006771484,0.00456783,0.01841826],"genre_scores_gemma":[0.8868441,0.00019679,0.1095736,0.002844797,0.0001991412,0.00006457933,0.0000172607,0.00001739862,0.0002422834],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7657956,"threshold_uncertainty_score":0.661857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01748919794725137,"score_gpt":0.2473667300507126,"score_spread":0.2298775321034613,"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."}}