{"id":"W3127642832","doi":"10.5334/dsj-2021-007","title":"Stewardship Maturity Assessment Tools for Modernization of Climate Data Management","year":2021,"lang":"en","type":"article","venue":"Data Science Journal","topic":"Climate variability and models","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Environment and Climate Change Canada; Centrum fÖr Personcentrerad Vård; National Centers for Environmental Information; National Oceanic and Atmospheric Administration; Grains Research and Development Corporation; National Aeronautics and Space Administration","keywords":"Stewardship (theology); Data management; Maturity (psychological); Scope (computer science); Data quality; Computer science; Process (computing); Quality (philosophy); Usability; Process management; Environmental resource management; Business; Data science; Database; Environmental science; Political science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06702811,0.001818128,0.0008809016,0.01949688,0.00256715,0.01178093,0.002727371,0.001504677,0.008000716],"category_scores_gemma":[0.1933127,0.00108657,0.00231012,0.01139637,0.001570618,0.01648218,0.009145082,0.004929029,0.002882995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006069065,"about_ca_system_score_gemma":0.0122671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006120121,"about_ca_topic_score_gemma":0.008141815,"domain_scores_codex":[0.9556321,0.01534082,0.009628084,0.002915379,0.01508084,0.001402759],"domain_scores_gemma":[0.7991746,0.0875411,0.02238633,0.02474849,0.06061643,0.005533036],"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":[0.0001805907,0.0008248533,0.03693908,0.002072768,0.0002312234,0.0002507309,0.01360639,0.01057737,0.004264905,0.1437232,0.0655937,0.7217353],"study_design_scores_gemma":[0.0001655622,0.0006451487,0.04811697,0.005358081,0.0002559234,0.0008074783,0.01770723,0.119172,0.01590688,0.2615226,0.5296992,0.0006429623],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03421797,0.0009922758,0.8816442,0.00739603,0.0003181824,0.004818649,0.005332114,0.01605628,0.04922415],"genre_scores_gemma":[0.07501145,0.0004777446,0.9125686,0.0003141311,0.00005650412,0.00229364,0.005752932,0.0007836084,0.002741354],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06702811,"threshold_uncertainty_score":0.3544827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.132977886381932,"score_gpt":0.3695392086253232,"score_spread":0.2365613222433913,"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."}}