{"id":"W4247160683","doi":"10.5194/hess-2016-562","title":"Spatial characterization of long-term hydrological change in the Arkavathy watershed adjacent to Bangalore, India","year":2016,"lang":"en","type":"preprint","venue":"","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":4,"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; Surface water; Rainwater harvesting; Hydrology (agriculture); Groundwater; Water resource management; Urbanization; Water resources; Water scarcity; Drainage basin; Agriculture; Geography; Environmental engineering; Geology; Ecology; Cartography","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":[0.0001488985,0.0001687905,0.0001936756,0.001387609,0.0003382347,0.0006856707,0.0004083922,0.0001585156,0.0006824749],"category_scores_gemma":[0.0004618202,0.0001401945,0.0002435545,0.002499295,0.0004156513,0.0002899969,0.0005039402,0.0002431876,0.0002396021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007524592,"about_ca_system_score_gemma":0.0006359363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1222266,"about_ca_topic_score_gemma":0.2158155,"domain_scores_codex":[0.9998473,0.00001814884,0.00001227964,0.00004628308,0.0000286707,0.0000472892],"domain_scores_gemma":[0.9995223,0.00009910781,0.00009824722,0.0000642357,0.0001533196,0.00006263391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001024615,0.00007890075,0.9693851,0.0001110194,0.0000777557,0.0004425853,0.001625362,0.005008894,0.00621336,0.0002186235,0.001529096,0.01520683],"study_design_scores_gemma":[0.000001410639,0.000007163103,0.9965441,0.000005917706,0.000009292119,0.00004856985,0.0007186984,0.002011421,0.0002082919,0.00001889297,0.0004193592,0.00000690159],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964729,0.00004434562,0.0001980785,0.00004277319,0.000003280143,0.000008624932,0.00234867,0.00004369997,0.0008375316],"genre_scores_gemma":[0.9972363,0.00003409806,0.0002947202,0.00001215069,0.000003347196,0.00001163429,0.002174971,0.000005425114,0.0002273105],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1222266,"threshold_uncertainty_score":0.2430303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02285823954321227,"score_gpt":0.2567760526730312,"score_spread":0.2339178131298189,"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."}}