{"id":"W2883773897","doi":"10.1016/j.scitotenv.2018.07.069","title":"A novel index for assessment of riparian strip efficiency in agricultural landscapes using high spatial resolution satellite imagery","year":2018,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Soil erosion and sediment transport","field":"Agricultural and Biological Sciences","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Riparian zone; Environmental science; Buffer strip; Surface runoff; Hydrology (agriculture); Water quality; Riparian buffer; Land cover; Remote sensing; Streamflow; Riparian forest; Erosion; Vegetation (pathology); Nonpoint source pollution; Bank; Drainage basin; Land use; Geography; Ecology; Geology; Cartography; Habitat","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":[0.0007514571,0.0004605159,0.0004694694,0.004865473,0.0002592976,0.001488431,0.0006210222,0.0005812467,0.00100513],"category_scores_gemma":[0.001362674,0.0002059726,0.000326739,0.003262837,0.0002822597,0.001408469,0.0006283607,0.0002650187,0.0003042392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006053775,"about_ca_system_score_gemma":0.000363253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002119069,"about_ca_topic_score_gemma":0.004077652,"domain_scores_codex":[0.9995533,0.00005312699,0.00005008756,0.00009638182,0.0002052094,0.00004198652],"domain_scores_gemma":[0.9991593,0.0002687294,0.0002114288,0.0000743022,0.0002188124,0.00006743481],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005927642,0.0007112138,0.3990819,0.0003830708,0.0008247449,0.0003566508,0.0002923061,0.06397978,0.07195851,0.003622432,0.005793916,0.4524027],"study_design_scores_gemma":[0.0000544662,0.0002939286,0.4152506,0.00004622534,0.00027149,0.0007129621,0.0003718275,0.5627909,0.01356755,0.00236612,0.004165109,0.000108854],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8010992,0.0008915483,0.1881653,0.0001157989,0.00005545729,0.0001781813,0.003003718,0.0007447795,0.005745985],"genre_scores_gemma":[0.9153852,0.0002256421,0.08093619,0.0000309611,0.00004951713,0.0001179705,0.002086864,0.00004729033,0.00112025],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004865473,"threshold_uncertainty_score":0.004392326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02337602411267771,"score_gpt":0.2314479190899139,"score_spread":0.2080718949772362,"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."}}