{"id":"W2890622483","doi":"10.23889/ijpds.v3i4.835","title":"Establishing an International Data Linkage Repository Workgroup Toward a Benchmarking Repository","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Workgroup; Benchmarking; Computer science; Record linkage; Linkage (software); Data science; Metadata; Field (mathematics); Custodians; Information repository; Data mining; World Wide Web; Computer data storage; Business","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","scholarly_communication","open_science"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.01858557,0.0001968708,0.0002186745,0.0009445667,0.001417079,0.009656461,0.02526368,0.00007588558,0.0001697458],"category_scores_gemma":[0.01037245,0.0001688334,0.00006958714,0.0007760925,0.0004997102,0.03431165,0.005978808,0.0002993075,0.00003891655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002812394,"about_ca_system_score_gemma":0.0003169796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004699212,"about_ca_topic_score_gemma":0.0001889311,"domain_scores_codex":[0.9909574,0.0002323833,0.001521213,0.001603127,0.005276224,0.0004095967],"domain_scores_gemma":[0.9920226,0.0006548192,0.001247477,0.003403968,0.002354161,0.000317009],"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":[0.000671382,0.0007151362,0.1144929,0.00001422428,0.0003150937,0.0002175942,0.002675783,0.001054542,0.004411707,0.05184084,0.1451311,0.6784597],"study_design_scores_gemma":[0.0008178101,0.0001872962,0.1074025,0.0001508702,0.00004351126,0.0004967618,0.00134626,0.3945343,0.0002558484,0.01261585,0.4816495,0.00049945],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2121336,0.00009457808,0.6520772,0.006568337,0.1112014,0.0007887345,0.00298349,0.0002100583,0.01394261],"genre_scores_gemma":[0.9360772,0.00002838669,0.05110387,0.0007719413,0.008440174,0.00000583631,0.002527776,0.00001709194,0.001027713],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7239437,"threshold_uncertainty_score":0.9998829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4002997965960512,"score_gpt":0.5237836597062985,"score_spread":0.1234838631102472,"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."}}