{"id":"W4402406479","doi":"10.23889/ijpds.v9i5.2865","title":"Efficiency gains from the implementation of Research IDs for data linkage: A case study from Population Data BC and the Data Innovation Program in British Columbia, Canada","year":2024,"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":"","funders":"","keywords":"Linkage (software); Record linkage; Linked data; Population; Computer science; Data science; Research data; World Wide Web; Demography; Biology; Sociology; Genetics; Data curation","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08256084,0.0006186957,0.0008573147,0.002819404,0.01767866,0.01017086,0.005766498,0.002339278,0.0032918],"category_scores_gemma":[0.1271124,0.0009750674,0.0007413956,0.008894714,0.005322119,0.00399987,0.008231238,0.004481755,0.0003655282],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.1416212,"about_ca_system_score_gemma":0.292949,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9496703,"about_ca_topic_score_gemma":0.9674171,"domain_scores_codex":[0.8786376,0.07467704,0.004785299,0.004033848,0.02049211,0.01737411],"domain_scores_gemma":[0.8100283,0.09316619,0.009465598,0.0149031,0.0557835,0.01665332],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"case_report","study_design_scores_codex":[0.001172839,0.003263116,0.3992634,0.002216676,0.0004335758,0.006702139,0.1550252,0.006896277,0.001410971,0.02175573,0.03035829,0.3715018],"study_design_scores_gemma":[0.0005674765,0.001324884,0.506595,0.002280397,0.0005299061,0.001796077,0.3484974,0.01087985,0.002350536,0.005108399,0.1197099,0.0003601432],"study_design_candidate":"case_report","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9365861,0.001530312,0.005027317,0.02181676,0.00008824999,0.002377818,0.0007543337,0.0001800748,0.03163903],"genre_scores_gemma":[0.974766,0.001184958,0.01232496,0.002726195,0.00003363795,0.0008584122,0.0004267773,0.0001141472,0.007564906],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9174392,"threshold_uncertainty_score":0.9955977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4921450913277475,"score_gpt":0.6045446402558524,"score_spread":0.1123995489281049,"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."}}