{"id":"W4362455788","doi":"10.5539/enrr.v13n1p1","title":"Spatiotemporal Dynamic of Land Use/Land Cover Changes and Their Drivers in the Fincha&amp;#39; a-Neshe Sub-Basin, Southeastern Blue Nile Basin, Ethiopia","year":2023,"lang":"en","type":"article","venue":"Environment and Natural Resources Research","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Shrubland; Land cover; Deforestation (computer science); Land use; Grassland; Wetland; Geography; Structural basin; Population; Physical geography; Environmental science; Remote sensing; Ecology; Ecosystem; Computer science; Geology","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":[0.001320191,0.0001875851,0.0002336599,0.0001503522,0.0002661982,0.0001033682,0.0003108466,0.0001284285,0.0002116461],"category_scores_gemma":[0.00003351174,0.0001088788,0.00003965352,0.0003254315,0.0002505228,0.00017962,0.000477826,0.0004039413,0.0001815259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004802899,"about_ca_system_score_gemma":0.000005100587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00281422,"about_ca_topic_score_gemma":0.009800184,"domain_scores_codex":[0.9978701,0.0003922986,0.0001995149,0.0004025676,0.0006641183,0.000471374],"domain_scores_gemma":[0.9989907,0.0005504839,0.00006961481,0.0002925661,0.000005477604,0.0000911623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009857371,0.0000326788,0.9862171,0.00006253697,0.00001645687,0.00001051118,0.008472671,0.0002688904,0.001513783,0.0000014844,0.0001747619,0.003130494],"study_design_scores_gemma":[0.0005608938,0.000121255,0.9570255,0.00006319131,0.000007110016,0.000006128388,0.00121948,0.005190538,0.0003266855,0.0000713273,0.03523666,0.0001712287],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975079,0.0005561137,0.000001434361,0.001357147,0.00003587156,0.0003760801,0.0000512976,0.00001459318,0.00009952635],"genre_scores_gemma":[0.997592,0.001603763,0.00001021075,0.0000775983,0.00003605179,0.00002091259,0.00004667474,0.00001653646,0.0005962338],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03506189,"threshold_uncertainty_score":0.5468732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02738790860121039,"score_gpt":0.2620561685491728,"score_spread":0.2346682599479624,"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."}}