{"id":"W2783348683","doi":"10.1038/s41467-017-02755-y","title":"Ambient PM2.5 exposure and expected premature mortality to 2100 in India under climate change scenarios","year":2018,"lang":"en","type":"article","venue":"Nature Communications","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":198,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Langley Research Center; National Aeronautics and Space Administration; Indian Institute of Technology Delhi; Lawrence Livermore National Laboratory; Dalhousie University; International Institute for Applied Systems Analysis; Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Baseline (sea); Climate change; Per capita; Environmental science; Particulates; Coupled model intercomparison project; Socioeconomic status; Representative Concentration Pathways; Population; Environmental health; Climate model; Demography; Medicine; Biology; Ecology","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":[],"consensus_categories":[],"category_scores_codex":[0.0004898135,0.0004030049,0.0001743406,0.0005797863,0.0002226082,0.0006955164,0.0005866038,0.0004027003,0.001335262],"category_scores_gemma":[0.001026354,0.0001933634,0.0008867308,0.0008174396,0.0002623061,0.0004258482,0.0005775547,0.0004111738,0.0002282158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001282256,"about_ca_system_score_gemma":0.0007893693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05881747,"about_ca_topic_score_gemma":0.03255537,"domain_scores_codex":[0.9997894,0.00005147505,0.00001380418,0.0000385194,0.00002843031,0.00007833094],"domain_scores_gemma":[0.9995406,0.000162997,0.00008544093,0.00004427835,0.00011162,0.00005513998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007977848,0.00009521173,0.5701028,0.0001697346,0.0004849643,0.0008332015,0.0002272216,0.4098729,0.001613459,0.003517742,0.004635234,0.007649821],"study_design_scores_gemma":[0.00007507866,0.0002279298,0.7913526,0.00005830639,0.0002726933,0.0004522498,0.0009121175,0.1973063,0.002637048,0.003391112,0.003221365,0.00009309039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99118,0.0001337453,0.0009444433,0.0002636015,0.00001926045,0.00001047397,0.004528372,0.00008238245,0.002837677],"genre_scores_gemma":[0.99765,0.00005450589,0.0002005319,0.0000243113,0.000003967727,0.000008950071,0.001840107,0.000005091987,0.0002126475],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05881747,"threshold_uncertainty_score":0.1169502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07246139213441406,"score_gpt":0.369207633273355,"score_spread":0.296746241138941,"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."}}