{"id":"W2770263991","doi":"10.1080/01639374.2017.1388895","title":"One for Nine Ten: Cataloging for Consortia Collections, a UC model","year":2017,"lang":"en","type":"article","venue":"Cataloging & Classification Quarterly","topic":"Library Science and Information Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of British Columbia","keywords":"Cataloging; Library science; Resource Description and Access; Computer science; Redundancy (engineering); World Wide Web; Digital collections; Operating system","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.000724167,0.0001800797,0.0002527078,0.0002392859,0.00233082,0.002231208,0.001846553,0.0001047776,0.000002962647],"category_scores_gemma":[0.0001825213,0.0001806513,0.0001307959,0.0002518206,0.0001636545,0.006314352,0.00005884097,0.00009075968,0.00005649963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008228513,"about_ca_system_score_gemma":0.0003140149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002379794,"about_ca_topic_score_gemma":0.00001008193,"domain_scores_codex":[0.9981733,0.00002497613,0.0005916893,0.0005368561,0.0002533377,0.0004198292],"domain_scores_gemma":[0.9972094,0.00015926,0.0006893176,0.001480328,0.000320244,0.0001414189],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001114233,0.0003648225,0.001656246,0.0004160021,0.0001355286,0.000001421722,0.02163873,0.001044139,0.02216417,0.6079221,0.1380239,0.2065215],"study_design_scores_gemma":[0.0008406375,0.0003017434,0.002536045,0.00004333754,0.000008737929,0.000009663291,0.0009149508,0.9718102,0.001188462,0.006595042,0.01540344,0.0003477869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003484105,0.00003333947,0.9865202,0.005061502,0.0006587069,0.001132796,0.0001248791,0.0003151735,0.002669297],"genre_scores_gemma":[0.947558,0.000003941459,0.04618032,0.0005284034,0.0001592408,0.0008716995,0.0003025695,0.00001536102,0.00438051],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.970766,"threshold_uncertainty_score":0.998968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09707613412315634,"score_gpt":0.3002213126480816,"score_spread":0.2031451785249252,"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."}}