{"id":"W2753993218","doi":"10.5194/hess-2017-543","title":"Proximate and underlying drivers of socio-hydrologic change in the upper Arkavathy watershed, India","year":2017,"lang":"en","type":"article","venue":"","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Tata Trusts; United States Agency for International Development; International Development Research Centre; Centro Nacional de Investigaciones Cardiovasculares; Ministry of Earth Sciences; National Science Foundation","keywords":"Watershed; Groundwater; Water security; Water resources; Population; Urbanization; Hydrology (agriculture); Water resource management; Environmental science; Surface water; Geography; Environmental resource management; Ecology; Geology","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":[0.0002040122,0.0001164846,0.0001621893,0.0006755447,0.0005310086,0.001039188,0.0005015865,0.0002389651,0.0009023813],"category_scores_gemma":[0.0006145334,0.000129628,0.0002839616,0.001212193,0.0008708801,0.0004103788,0.0007616828,0.000389503,0.00009071628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00198382,"about_ca_system_score_gemma":0.001312434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1215874,"about_ca_topic_score_gemma":0.1645777,"domain_scores_codex":[0.9998434,0.00003274508,0.000009256121,0.00003480288,0.00001951773,0.00006018907],"domain_scores_gemma":[0.9996344,0.000131214,0.00007270683,0.00003427903,0.00007097654,0.000056439],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007333518,0.0001085592,0.9698802,0.00004666975,0.00005815427,0.001334756,0.002652277,0.01311453,0.003346454,0.001715154,0.0005713445,0.007098501],"study_design_scores_gemma":[0.00000372033,0.00002498463,0.9795314,0.00001023819,0.0000241299,0.0001525208,0.005996647,0.01263294,0.0003317702,0.0004522288,0.000820015,0.0000192651],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990907,0.00001492398,0.0001016451,0.00008112453,0.000001265424,0.0000046224,0.0001443799,0.000008055653,0.0005532606],"genre_scores_gemma":[0.9997399,0.00001374539,0.00006843415,0.000005023458,8.613964e-7,0.000003063553,0.00006701171,0.000001135258,0.0001008118],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1215874,"threshold_uncertainty_score":0.2417594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04433988947776856,"score_gpt":0.2758057169899611,"score_spread":0.2314658275121926,"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."}}