{"id":"W7100167951","doi":"","title":"National Libtary Bibliothèque nationale du Canada Acquisitions and Acquisitions et","year":2015,"lang":"en","type":"article","venue":"","topic":"Library Science and Information Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Payment; Context (archaeology); Agency (philosophy); Legislation","routes":{"ca_aff":false,"ca_fund":false,"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":[0.0003951152,0.00007458506,0.00006877889,0.0003833138,0.000174928,0.0005310639,0.0004165493,0.00002587979,0.0001257902],"category_scores_gemma":[0.00006152751,0.00006420916,0.00001668311,0.001202354,0.00003305166,0.008735077,0.00016966,0.00004741572,0.00007051922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007738532,"about_ca_system_score_gemma":0.00282055,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01471149,"about_ca_topic_score_gemma":0.01216148,"domain_scores_codex":[0.9988037,0.0000412451,0.0002059858,0.0001606759,0.0006557677,0.0001326061],"domain_scores_gemma":[0.9991369,0.0001246043,0.00005525227,0.0001493593,0.0003400293,0.0001938169],"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":[4.775119e-7,0.00001098475,0.0006184094,0.000002001954,0.000004083691,0.000001777335,0.0007998167,0.000384058,0.000006989667,0.4856463,0.5124404,0.00008467661],"study_design_scores_gemma":[0.0007538123,0.00007761311,0.02109552,0.00001866461,0.000001486626,0.0003786484,0.001180318,0.2525043,0.0002347766,0.03395037,0.6893216,0.0004828082],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0195472,0.0001987476,0.241891,0.07006048,0.001067622,0.0002735267,0.00009386914,0.0003168586,0.6665508],"genre_scores_gemma":[0.9583054,0.00001830067,0.009655681,0.02791907,0.0001499633,0.00002376984,0.0000540377,0.000003707309,0.003870067],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9387582,"threshold_uncertainty_score":0.9918496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02261011882735173,"score_gpt":0.2291684707514046,"score_spread":0.2065583519240529,"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."}}