{"id":"W7095928643","doi":"","title":"Acquisitions and Acquisitions et Bibliographie Services setvices bibliographiques","year":2015,"lang":"en","type":"article","venue":"","topic":"Library Collection Development and Digital Resources","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Order (exchange); Government (linguistics); Legislation; Subject (documents)","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":["insufficient_payload"],"category_scores_codex":[0.003482459,0.001051154,0.00129991,0.0248029,0.002762899,0.01576814,0.001130198,0.00114851,0.4792359],"category_scores_gemma":[0.01377252,0.0008677548,0.0006059088,0.05159331,0.00107008,0.00562202,0.00298475,0.001916193,0.3951835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006263741,"about_ca_system_score_gemma":0.01399991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02900673,"about_ca_topic_score_gemma":0.022504,"domain_scores_codex":[0.994408,0.0007589202,0.0007035753,0.0008001642,0.002889772,0.0004396599],"domain_scores_gemma":[0.9916326,0.001182498,0.0007603171,0.001551574,0.004095356,0.0007777005],"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":[0.00005672824,0.00002192011,0.001261287,0.0006925634,0.00001508685,0.00005365444,0.0005036286,0.0001034049,0.0005553229,0.02708003,0.7996868,0.1699695],"study_design_scores_gemma":[0.000003464424,0.000003278405,0.001382406,0.0000858608,0.000002527913,0.00003264856,0.0001145813,0.0000341253,0.0001269285,0.0004359361,0.9977723,0.000005991464],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.002381798,0.007430987,0.00254538,0.004106986,0.003140652,0.0003768859,0.1018184,0.004603256,0.8735956],"genre_scores_gemma":[0.01509207,0.01376722,0.004412177,0.0005869257,0.001440084,0.0004999192,0.08612031,0.003476337,0.8746049],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.5207641,"threshold_uncertainty_score":0.7428068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02250574147322785,"score_gpt":0.2514026203707282,"score_spread":0.2288968788975003,"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."}}