{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002775268,0.0009590713,0.001395174,0.008218609,0.01122803,0.01438222,0.001618637,0.001901772,0.1216034],"category_scores_gemma":[0.009878634,0.0008670184,0.000953672,0.01216153,0.001908779,0.002868414,0.002531765,0.003344755,0.01703615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.1072458,"about_ca_system_score_gemma":0.2206572,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.9889254,"about_ca_topic_score_gemma":0.9894265,"domain_scores_codex":[0.9925753,0.0003426601,0.00023676,0.0006426641,0.004883108,0.001319538],"domain_scores_gemma":[0.9888615,0.0009544007,0.0004313974,0.0004265833,0.008082729,0.001243278],"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":[0.0001981864,0.00009846701,0.01310221,0.0006477209,0.00007044028,0.0002357404,0.001186595,0.0005589191,0.001001462,0.1170523,0.717337,0.1485111],"study_design_scores_gemma":[0.00001256518,0.000007962059,0.02376141,0.0001564168,0.00001788196,0.00005746087,0.0005298089,0.000202989,0.0006654701,0.0006335528,0.9739207,0.00003375361],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.01972726,0.02712782,0.002395492,0.02396267,0.002229032,0.0003707631,0.04662332,0.001364007,0.8761997],"genre_scores_gemma":[0.03961272,0.008729812,0.002830544,0.001193836,0.0001501722,0.0001294323,0.006737203,0.0003710384,0.9402452],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.9889254,"threshold_uncertainty_score":0.7781261,"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."}}