{"id":"W7096747995","doi":"","title":"National Library I*I ofCamda Biblioîhèque nationale du Canada Acquisitions and Acquisitions et","year":2000,"lang":"en","type":"article","venue":"","topic":"Library Collection Development and Digital Resources","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"National library; Government (linguistics); Information system; Context (archaeology)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00005927587,0.0001047435,0.00007488767,0.0004233488,0.0003047019,0.0007723219,0.000298714,0.00003139882,0.00522548],"category_scores_gemma":[0.00001848657,0.00009752827,0.00002551016,0.001705354,0.0000302913,0.004265198,0.0001247404,0.00005522386,0.00005529494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000329228,"about_ca_system_score_gemma":0.001372825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002311684,"about_ca_topic_score_gemma":0.005199049,"domain_scores_codex":[0.9989821,0.00003168319,0.0001686386,0.0002483505,0.0004191835,0.0001500508],"domain_scores_gemma":[0.9994413,0.0002367327,0.0000262574,0.0001086778,0.00005782558,0.0001292632],"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":[0.000003908955,0.00004525826,0.001535859,0.000002960183,0.00001699375,0.000008160228,0.0001242224,0.000187858,0.000004838003,0.3741558,0.6216263,0.002287876],"study_design_scores_gemma":[0.0003389405,0.00002248082,0.03745785,0.000009015426,0.000001651253,0.00008125021,0.00002188905,0.009799874,0.0002286678,0.03448088,0.9172779,0.000279636],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.03343573,0.0001238379,0.002957442,0.02474952,0.0001015655,0.0001148414,0.00004922756,0.0002932171,0.9381746],"genre_scores_gemma":[0.7470075,0.0002308723,0.01641064,0.02844566,0.0001592853,0.00005285895,0.000145509,0.0000154204,0.2075322],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.7306424,"threshold_uncertainty_score":0.9956839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006340525383828138,"score_gpt":0.1849057037951024,"score_spread":0.1785651784112743,"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."}}