{"id":"W6949727091","doi":"10.5281/zenodo.3596717","title":"DDI 4 Core: Describing and managing data for traditional and modern data platforms","year":2019,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","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":["scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.004522624,0.0001067663,0.0001536373,0.0001973514,0.001193963,0.001944866,0.003158677,0.00003339691,0.002190067],"category_scores_gemma":[0.001856368,0.00009451608,0.0000142227,0.0002776558,0.0001451872,0.001951154,0.006302258,0.0001217332,0.001131341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000267512,"about_ca_system_score_gemma":0.000003558019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001010724,"about_ca_topic_score_gemma":0.000001399565,"domain_scores_codex":[0.9977746,0.0000839072,0.0003160523,0.0008816713,0.000683086,0.0002606804],"domain_scores_gemma":[0.9975126,0.0002204209,0.0001172358,0.001823504,0.0001911707,0.0001350656],"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":[0.00008974049,0.00006636821,0.00001990695,0.00006703589,0.00004311755,0.00000383126,0.0006131373,0.00002998756,0.0003870423,0.06588905,0.4116491,0.5211417],"study_design_scores_gemma":[0.0005946701,0.0001004824,0.0005099982,0.00002509917,0.00001469816,0.00003662279,0.0009778967,0.0795122,0.000007851774,0.02799598,0.8900793,0.0001452174],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1822036,0.0007872034,0.6341493,0.009872888,0.0008447517,0.004485432,0.04305874,0.001219679,0.1233784],"genre_scores_gemma":[0.9456076,0.0002796829,0.006127536,0.0009451685,0.0002179647,9.224924e-8,0.04340613,0.0007536772,0.002662151],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.763404,"threshold_uncertainty_score":0.9996464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6154124851776948,"score_gpt":0.3743942424592931,"score_spread":0.2410182427184017,"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."}}