{"id":"W6912667207","doi":"10.5281/zenodo.3596718","title":"DDI 4 Core: Describing and managing data for traditional and modern data platforms","year":2019,"lang":"en","type":"article","venue":"Figshare","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada","funders":"","keywords":"Interoperability; XML; Scope (computer science); Data management; Data model (GIS); Data system; Metadata; Core (optical fiber)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009374965,0.00009219773,0.0001455406,0.00007490504,0.0001314844,0.0004970353,0.001696831,0.0000336108,0.01505071],"category_scores_gemma":[0.002107098,0.00007222193,0.0000126131,0.00009771921,0.0000121637,0.002259385,0.002147256,0.00006285879,0.000488389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007224749,"about_ca_system_score_gemma":0.00001941436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004495433,"about_ca_topic_score_gemma":0.00002923378,"domain_scores_codex":[0.9983783,0.00001074114,0.000238481,0.000720262,0.0004796588,0.0001725524],"domain_scores_gemma":[0.9971456,0.0009016998,0.00009880561,0.001736127,0.00004335793,0.0000743878],"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":[0.00001335332,0.00001258556,0.00006386849,0.00007572037,0.0000139499,0.000002221811,0.00008410415,0.000008476771,0.000006342655,0.002274844,0.904192,0.09325257],"study_design_scores_gemma":[0.0005816898,0.00003780726,0.002041508,0.0003725655,0.00001323793,0.00001005077,0.00054851,0.3097344,0.000005215476,0.06786211,0.6185783,0.0002146115],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002527029,0.0002874992,0.001625549,0.0004553361,0.00009143242,0.0004712947,0.9925627,0.00002769625,0.001951531],"genre_scores_gemma":[0.1351508,0.00001436894,0.003091586,0.001077564,0.0001177773,0.00003138901,0.8592983,0.00001407741,0.001204094],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3097259,"threshold_uncertainty_score":0.9858497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8478209347699858,"score_gpt":0.4379830149435481,"score_spread":0.4098379198264377,"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."}}