{"id":"W1489611604","doi":"10.1300/j104v45n02_02","title":"Making the Link: AACR to RDA: Part 1: Setting the Stage","year":2007,"lang":"en","type":"article","venue":"Cataloging & Classification Quarterly","topic":"Library Science and Information Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Cataloging; Library science; Computer science; Steering committee; Resource Description and Access; Schedule; Joint (building); Resource (disambiguation); World Wide Web; Engineering management; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003002173,0.0001456196,0.00010887,0.0001243965,0.0008235941,0.001165092,0.002212977,0.00005571379,0.000009972369],"category_scores_gemma":[0.00007222238,0.00008477525,0.00006494007,0.001004554,0.00009969542,0.002713189,0.00009652353,0.0002059757,0.0007075955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006235085,"about_ca_system_score_gemma":0.00009776063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001158379,"about_ca_topic_score_gemma":0.000008090422,"domain_scores_codex":[0.9980333,0.0001042605,0.0005792928,0.0003473723,0.0004860079,0.0004497131],"domain_scores_gemma":[0.997921,0.0003353885,0.0003212359,0.001213782,0.0001097268,0.00009883714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000006651622,0.00001813925,0.002319246,0.00002467656,0.00001651466,0.000005637486,0.09534977,0.0001473552,0.001999042,0.2238505,0.02824847,0.6480139],"study_design_scores_gemma":[0.000160265,0.0001377551,0.0617888,0.00006218452,0.0000029463,0.00003256408,0.02435918,0.03939961,0.0005455561,0.0007512986,0.8724026,0.0003572597],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02462425,0.00006187903,0.9185486,0.04466446,0.001429492,0.0005896277,0.000008086827,0.0003848866,0.009688671],"genre_scores_gemma":[0.9913381,9.772821e-7,0.002094282,0.005141659,0.0004562901,0.00003935518,0.00001602839,0.000007337272,0.0009059286],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9667139,"threshold_uncertainty_score":0.9998718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06884557485468633,"score_gpt":0.2998486112228737,"score_spread":0.2310030363681874,"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."}}