{"id":"W2514999749","doi":"10.5539/enrr.v6n3p65","title":"Characterization of Chyulu Hills Watershed Ecosystem Services in South-Eastern Kenya","year":2016,"lang":"en","type":"article","venue":"Environment and Natural Resources Research","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Southeast University","keywords":"Hydrology (agriculture); Watershed; Groundwater; Environmental science; Surface water; Borehole; Water resources; Geology; Environmental engineering; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007346456,0.0001324791,0.0001819936,0.0001388682,0.0001312033,0.00001622373,0.0002445383,0.00007577369,0.0005509607],"category_scores_gemma":[0.0000106712,0.00008028682,0.00002914155,0.0001235868,0.0003287727,0.0002125856,0.0006704695,0.0001433043,0.0003281095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008330168,"about_ca_system_score_gemma":8.963956e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006023164,"about_ca_topic_score_gemma":0.00007145889,"domain_scores_codex":[0.9982883,0.0002232432,0.00023588,0.0003731529,0.0004696377,0.0004097245],"domain_scores_gemma":[0.9995957,0.00007574321,0.00006649292,0.0001961525,0.000003399525,0.00006250434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001581939,0.00004784652,0.8284277,0.0000526828,0.00002348283,0.00001007408,0.004804669,0.00001612342,0.1591967,0.00001098513,0.00001600779,0.007235628],"study_design_scores_gemma":[0.0008652814,0.0001363281,0.9607276,0.00008578148,0.000007542293,0.000001081223,0.0004401119,0.0004227287,0.01138438,0.0001122005,0.02563607,0.0001808808],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980744,0.0001147973,0.000007852004,0.0008675849,0.00003176881,0.0002909593,0.00001011308,0.00001072225,0.0005918564],"genre_scores_gemma":[0.9958789,0.0003739499,0.00001425101,0.00003232707,0.00002796236,0.00002425533,0.00001024682,0.000009934567,0.003628178],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1478123,"threshold_uncertainty_score":0.6032633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01232170051750337,"score_gpt":0.234533518591756,"score_spread":0.2222118180742527,"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."}}