{"id":"W7097225146","doi":"","title":"IDENTIFIERS *Canada; IFLA 1974; Library Developme.,t; Library Statistics","year":2016,"lang":"en","type":"article","venue":"","topic":"Library Science and Administration","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Subject (documents); Identifier; Table (database); Library automation; Distribution (mathematics); Information system; Automation","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"other","about_ca_system":false,"about_ca_topic":true,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["insufficient_payload"],"domain":null,"study_design":"not_applicable","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001336157,0.00009824563,0.0000936471,0.0000530361,0.0004366162,0.0003483528,0.0004721411,0.00006156407,0.0124886],"category_scores_gemma":[0.0001009428,0.00006701557,0.00002282362,0.0003821825,0.000265094,0.006002682,0.00007328179,0.00004854906,0.0002084614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002886628,"about_ca_system_score_gemma":0.005196047,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01461594,"about_ca_topic_score_gemma":0.09614345,"domain_scores_codex":[0.9985782,0.00009860821,0.0002213915,0.0002488424,0.0004839626,0.0003690127],"domain_scores_gemma":[0.9992365,0.0002440938,0.00006719681,0.0001449915,0.00001581362,0.0002913956],"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":[0.000004798449,0.00001020337,0.028736,0.00000303369,0.00000481272,0.0000234426,0.0005211654,1.416068e-7,0.00007832235,0.2780522,0.6745785,0.01798737],"study_design_scores_gemma":[0.0001103143,0.0000151812,0.01722479,0.00001727047,0.000002604018,7.263254e-7,0.001340207,0.000004286167,0.002981128,0.0191135,0.9589878,0.0002022156],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.08410361,0.00005541978,0.003582365,0.08928295,0.001862655,0.0003220464,0.0002282485,0.0005818265,0.8199809],"genre_scores_gemma":[0.5750588,0.0002029113,0.01030101,0.004306975,0.0004077758,0.000006607823,0.00003988098,0.0000180086,0.4096581],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.4909551,"threshold_uncertainty_score":0.9919458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01660621376468223,"score_gpt":0.2481939414455485,"score_spread":0.2315877276808663,"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."}}