{"id":"W3138751991","doi":"10.7202/1075704ar","title":"Un aperçu de la recherche à Bibliothèque et Archives Canada","year":2021,"lang":"fr","type":"article","venue":"Archives","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Library and Archives Canada","funders":"National Science Board; York University; Social Sciences and Humanities Research Council of Canada; National Research Council Canada; University at Albany; University of Toronto; Strong; Centre National de la Recherche Scientifique; University of Windsor; National Science Foundation; University of Washington; McGill University; Arizona State University; Research Institute, Georgia Institute of Technology; Yale University; National Aeronautics and Space Administration; University of California, Los Angeles; University of Minnesota; University of British Columbia; U.S. Department of Defense","keywords":"Humanities; Political science; Art","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","bibliometrics","scholarly_communication","insufficient_payload"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.01363774,0.0003240422,0.0004790016,0.03391217,0.0004106283,0.004017131,0.002568543,0.0002682304,0.003260876],"category_scores_gemma":[0.09740758,0.0002825473,0.0002877468,0.1602695,0.001067077,0.0004847909,0.001667179,0.001924178,0.0001100234],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001982479,"about_ca_system_score_gemma":0.02217233,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.07420456,"about_ca_topic_score_gemma":0.07378136,"domain_scores_codex":[0.9817823,0.01123029,0.0007383939,0.001144195,0.003664282,0.001440563],"domain_scores_gemma":[0.813254,0.1838596,0.0001987923,0.001162822,0.0002559058,0.00126892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003908341,0.0004142939,0.0467184,0.00005255238,0.0001050768,0.0011343,0.002504019,0.0001386728,0.01136034,0.05205818,0.02634529,0.8591298],"study_design_scores_gemma":[0.0005067682,0.00008447267,0.1547121,0.00009820668,0.00001827801,0.0002647701,0.001491183,0.006461861,0.01325642,0.2033117,0.6193988,0.0003955122],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1345823,0.0843583,0.02658355,0.06312384,0.001783312,0.0002582296,0.0003341638,0.00004791857,0.6889284],"genre_scores_gemma":[0.566036,0.07822653,0.1333133,0.004152034,0.0004082982,0.0000301409,0.00002604269,0.00007172361,0.2177359],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8587343,"threshold_uncertainty_score":0.9999627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6317529281274052,"score_gpt":0.5616780748897119,"score_spread":0.07007485323769336,"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."}}