{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00653758,0.0007917269,0.0007182516,0.01449167,0.007346522,0.007986997,0.001887918,0.0009053162,0.05643627],"category_scores_gemma":[0.02492391,0.0006487425,0.0008729188,0.02197818,0.002655121,0.008312279,0.005241486,0.001594734,0.02157451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02607203,"about_ca_system_score_gemma":0.07661798,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8454502,"about_ca_topic_score_gemma":0.9078388,"domain_scores_codex":[0.9954913,0.0006566329,0.0006831306,0.0004151246,0.002067895,0.0006860195],"domain_scores_gemma":[0.9769959,0.004031911,0.0008490658,0.00334653,0.01365143,0.001125221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001226896,0.00004431836,0.003165358,0.0009047745,0.0000194823,0.000440669,0.01188666,0.0007155496,0.003937922,0.1743417,0.4055004,0.3989205],"study_design_scores_gemma":[0.000008635733,0.0000053196,0.00153389,0.0002096101,0.000009779672,0.00008109987,0.001184098,0.0002490825,0.0005789767,0.00358501,0.9925144,0.00004002852],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.01808955,0.005396687,0.1913075,0.01814209,0.001576846,0.001565814,0.04299638,0.03428146,0.6866438],"genre_scores_gemma":[0.1135069,0.01316141,0.465461,0.004672012,0.0009658359,0.001099457,0.05687037,0.009622638,0.3346403],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.992013,"threshold_uncertainty_score":0.3109199,"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."}}