{"id":"W2552939017","doi":"10.5194/hess-22-595-2018","title":"Spatial characterization of long-term hydrological change in the Arkavathy watershed adjacent to Bangalore, India","year":2018,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of International Science and Engineering; Centro Nacional de Investigaciones Cardiovasculares; International Development Research Centre; United States Agency for International Development; National Science Foundation","keywords":"Environmental science; Watershed; Precipitation; Urbanization; Hydrology (agriculture); Water resources; Surface water; Groundwater; Spatial variability; Water cycle; Water resource management; Geography; Ecology; Geology; Meteorology","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":[0.0001762418,0.000159561,0.0001862391,0.00135795,0.0003454552,0.0006550783,0.0004077859,0.0001477629,0.0005865417],"category_scores_gemma":[0.0005490933,0.0001314718,0.0002420565,0.002354644,0.0004064995,0.0003023818,0.0004747629,0.0002281499,0.000200057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000738328,"about_ca_system_score_gemma":0.0006647918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1157843,"about_ca_topic_score_gemma":0.2217642,"domain_scores_codex":[0.9998393,0.00002077174,0.0000142773,0.00004934735,0.00002876874,0.00004747046],"domain_scores_gemma":[0.9994138,0.0001273857,0.0001284416,0.00007129693,0.000189471,0.00006964109],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008304102,0.0000566525,0.9784458,0.00008114314,0.00006218785,0.000313222,0.001225853,0.003276492,0.004333302,0.000150445,0.0009930026,0.01097889],"study_design_scores_gemma":[0.000001344542,0.000007060072,0.9969169,0.000005748941,0.00000945988,0.00004813231,0.0006807409,0.001785129,0.0001896465,0.00001703623,0.000332479,0.000006295812],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971811,0.00003859805,0.0001825284,0.000037699,0.000002946389,0.00000713999,0.001895682,0.0000304935,0.0006238291],"genre_scores_gemma":[0.9976133,0.00002933469,0.000296457,0.00001029732,0.000003024936,0.00001027254,0.001850301,0.000004312823,0.0001827975],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1157843,"threshold_uncertainty_score":0.2302207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02284939863212723,"score_gpt":0.2380099591004164,"score_spread":0.2151605604682891,"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."}}