{"id":"W4402391115","doi":"10.23889/ijpds.v9i5.2502","title":"Development of a dictionary of information items inspired by common data models to support health research data access requests","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":"Université de Sherbrooke; Canadian Institute for Health Information","funders":"","keywords":"Computer science; Data access; Data science; Data mining; Information retrieval; Database","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","scholarly_communication","open_science"],"consensus_categories":["scholarly_communication","open_science"],"category_scores_codex":[0.0451072,0.0001049739,0.0002240757,0.001712902,0.0004478292,0.002046943,0.02069167,0.00003391594,0.00005319054],"category_scores_gemma":[0.004072517,0.00008639069,0.00002233855,0.001768724,0.0001862151,0.03706211,0.01151293,0.0001963772,0.00002805925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002524215,"about_ca_system_score_gemma":0.001509023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008483648,"about_ca_topic_score_gemma":0.0006455421,"domain_scores_codex":[0.9904437,0.0001763417,0.002132062,0.0007243376,0.006231599,0.0002919786],"domain_scores_gemma":[0.9942442,0.0005998697,0.0007220942,0.002675732,0.001537728,0.0002203543],"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":[0.00009869539,0.0001147922,0.0007033988,0.00004784731,0.00004573679,0.000001571114,0.001201977,0.00130419,0.0001621474,0.01906012,0.4119163,0.5653433],"study_design_scores_gemma":[0.0002289305,0.00008692306,0.004658162,0.000225908,0.000006021518,0.00001895834,0.0006804635,0.3638443,0.00008443063,0.01529787,0.6147435,0.0001244269],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03068523,0.0001687976,0.9033162,0.01223953,0.00492567,0.001045088,0.04699777,0.00004387703,0.0005778159],"genre_scores_gemma":[0.9092532,0.0001226452,0.04987905,0.0005486805,0.0001602007,0.0000111634,0.03986114,0.000009454456,0.0001545178],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8785679,"threshold_uncertainty_score":0.998989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8228812729718443,"score_gpt":0.6533858271484233,"score_spread":0.169495445823421,"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."}}