{"id":"W2997919243","doi":"10.14429/djlit.39.06.15227","title":"Open Data Resources for Clean Energy and Water Sectors in India","year":2019,"lang":"en","type":"article","venue":"DESIDOC Journal of Library & Information Technology","topic":"Research Data Management Practices","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Renewable Energy Laboratory; Office of Energy Efficiency; National Science and Technology Management Information System; Department of Science and Technology, Ministry of Science and Technology, India; U.S. Geological Survey; Global Affairs Canada; Office of Energy Efficiency and Renewable Energy; European Commission; U.S. Department of Energy; International Development Research Centre; European Environment Agency; Jawaharlal Nehru University; Nature Conservancy; Department for International Development; World Bank Group","keywords":"Open data; Nexus (standard); Open government; Corporate governance; Business; Commons; Data governance; Political science; Marketing; Computer science; Data quality","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.003868209,0.000131039,0.0001808375,0.004434245,0.002805257,0.00748136,0.001265632,0.000761139,0.006348693],"category_scores_gemma":[0.01223711,0.000244699,0.000347573,0.01143339,0.002244947,0.005520739,0.00901747,0.001094485,0.001501891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00431702,"about_ca_system_score_gemma":0.02181016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02443166,"about_ca_topic_score_gemma":0.02048609,"domain_scores_codex":[0.9948313,0.001581922,0.0007338997,0.0004309827,0.00168698,0.0007349902],"domain_scores_gemma":[0.9801127,0.008158064,0.002429711,0.00408797,0.002962068,0.002249492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002819045,0.0003392539,0.07355145,0.002199898,0.00006542719,0.002412751,0.02674422,0.002991993,0.004938326,0.4009468,0.07797125,0.4075568],"study_design_scores_gemma":[0.00002025283,0.00006985391,0.06969046,0.000844746,0.00004353241,0.000921148,0.03140209,0.001974964,0.004103634,0.02850984,0.8623111,0.0001084894],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.50187,0.00808481,0.01867881,0.06933714,0.0003700019,0.001056279,0.01927861,0.001987053,0.3793373],"genre_scores_gemma":[0.9433917,0.004567224,0.01635063,0.002255074,0.0001026406,0.0003443619,0.006308533,0.0001916248,0.02648802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9987344,"threshold_uncertainty_score":0.04857892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03680449269408433,"score_gpt":0.2928780703617647,"score_spread":0.2560735776676804,"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."}}