{"id":"W7095891443","doi":"","title":"National tibraw Bibliothwue nationale du Canada Acquisitions and Acquisitions et","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":"Legislation; Government (linguistics); Reproduction; Order (exchange); Control (management)","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":[0.001302074,0.001060125,0.001187217,0.004632323,0.00920275,0.01329928,0.001550852,0.001358151,0.3450691],"category_scores_gemma":[0.005080295,0.0006282812,0.0004658627,0.01167141,0.00193185,0.002735663,0.00240825,0.002603605,0.09197962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.039097,"about_ca_system_score_gemma":0.09995276,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9415969,"about_ca_topic_score_gemma":0.9709073,"domain_scores_codex":[0.9970586,0.00010554,0.00008918705,0.0003909319,0.001844855,0.0005109505],"domain_scores_gemma":[0.9948601,0.0002376234,0.0001983482,0.0002867535,0.003810779,0.0006065328],"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":[0.0000915482,0.00003260795,0.002245374,0.0004108528,0.00001907911,0.0002022457,0.0008159236,0.0001259365,0.0008511363,0.05322213,0.7828256,0.1591575],"study_design_scores_gemma":[0.000003844716,0.000003826922,0.00367683,0.00009995518,0.000003563949,0.00005330089,0.0003065547,0.0000782674,0.0002374102,0.000687641,0.9948332,0.00001556323],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.003863259,0.008843678,0.001989347,0.009462639,0.001976587,0.0001798978,0.03951237,0.001033775,0.9331385],"genre_scores_gemma":[0.00921853,0.003573829,0.001759032,0.000505293,0.00006019617,0.00005135807,0.005811992,0.000421011,0.9785987],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.3450691,"threshold_uncertainty_score":0.9341794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02116169125813537,"score_gpt":0.2213226445015206,"score_spread":0.2001609532433852,"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."}}