{"id":"W2730756006","doi":"10.5337/2017.207","title":"Mapping multiple climate-related hazards in South Asia","year":2017,"lang":"en","type":"report","venue":"","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"United Nations University Institute for Water, Environment, and Health","funders":"European Space Agency; United Nations Development Programme; U.S. Geological Survey; National Aeronautics and Space Administration","keywords":"Geography; Climatology; Physical geography; Cartography; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001139631,0.0005371015,0.0003332022,0.001901291,0.0004538253,0.00129973,0.0005056368,0.0003611207,0.001069993],"category_scores_gemma":[0.0008933397,0.0002409751,0.0006237155,0.00220897,0.00025651,0.001268598,0.002170078,0.0004809808,0.0001684972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008826951,"about_ca_system_score_gemma":0.00227076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02879887,"about_ca_topic_score_gemma":0.03707721,"domain_scores_codex":[0.9996343,0.00006870836,0.00002159696,0.00004829007,0.0001662607,0.00006089558],"domain_scores_gemma":[0.999348,0.0001581847,0.0001776984,0.0000531842,0.0001979045,0.00006501603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002553161,0.0002203722,0.6651012,0.0007110692,0.0006423583,0.002416936,0.005436699,0.1048041,0.01509165,0.00887389,0.003837959,0.1926083],"study_design_scores_gemma":[0.00008484594,0.0004175933,0.7402803,0.0005892976,0.000699607,0.0019138,0.02078,0.165737,0.01035824,0.01990138,0.03906628,0.0001715705],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9503915,0.001385625,0.02363638,0.001194683,0.0000329179,0.0001787904,0.00334671,0.0001402937,0.01969308],"genre_scores_gemma":[0.9715133,0.002191887,0.0206133,0.00007064249,0.00002127064,0.00009736798,0.00248396,0.00003087019,0.002977394],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02879887,"threshold_uncertainty_score":0.05726248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02221418764477902,"score_gpt":0.2672146983241112,"score_spread":0.2450005106793322,"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."}}