{"id":"W4414244142","doi":"10.2218/ijdc.v19i1.906","title":"A Maturity Model for Urban Dataset Metadata","year":2025,"lang":"en","type":"article","venue":"International Journal of Digital Curation","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Metadata; Documentation; Maturity (psychological); Capability Maturity Model; Relevance (law); Graph; Plug-in; Software","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"],"consensus_categories":[],"category_scores_codex":[0.001452179,0.00006764754,0.0001392364,0.0003243488,0.00004075329,0.001856884,0.001224317,0.00002635149,0.000019364],"category_scores_gemma":[0.003281104,0.00005074566,0.0001053437,0.0001409523,0.0000300392,0.009871976,0.0002063727,0.00006894155,0.00001652457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005129267,"about_ca_system_score_gemma":0.0001015394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002075463,"about_ca_topic_score_gemma":0.00001251014,"domain_scores_codex":[0.9979696,0.00002877164,0.0007531368,0.0001514374,0.00102278,0.00007426737],"domain_scores_gemma":[0.9980047,0.0003978865,0.0004546751,0.0002332036,0.0008715678,0.0000379319],"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":[0.0002582959,0.0001555936,0.0001708632,0.000004677159,0.0001571198,0.000004612083,0.00008910746,0.001312491,0.00005977263,0.2244516,0.738119,0.0352168],"study_design_scores_gemma":[0.0006515498,0.00004208569,0.0002039174,0.00002527054,0.00003044434,0.000008720334,0.0001848599,0.06703707,0.0001494042,0.4154451,0.5161462,0.0000753836],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002133158,0.0000491664,0.9800603,0.007966633,0.0009948907,0.0001119062,0.006940398,0.000005273505,0.001738303],"genre_scores_gemma":[0.9893944,0.00001054161,0.003216029,0.001046651,0.0001771098,0.000004113833,0.001877017,0.000003425577,0.00427073],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9872612,"threshold_uncertainty_score":0.9991793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2141903317703833,"score_gpt":0.4785366575588719,"score_spread":0.2643463257884886,"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."}}