{"id":"W4382681079","doi":"10.11159/iccste23.177","title":"Analysing Changes in Land Use and Land Cover (LULC) For C81 Catchment of the Free State, South Africa","year":2023,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Civil, Structural and Transportation Engineering","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Land use; Cover (algebra); Land cover; State (computer science); Drainage basin; Environmental science; Geography; Hydrology (agriculture); Computer science; Geology; Cartography; Engineering; Civil engineering; Algorithm","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":[0.00006712312,0.00006757947,0.00008548696,0.00003746892,0.00003134675,0.0000276946,0.000144051,0.00001816675,0.00001705752],"category_scores_gemma":[0.0000129357,0.00003936325,0.00002143925,0.0001154119,0.00001310646,0.0001419988,0.00002359842,0.00003892004,1.870674e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001249714,"about_ca_system_score_gemma":0.000002541525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000219148,"about_ca_topic_score_gemma":0.00155697,"domain_scores_codex":[0.999529,0.000001163637,0.0001262096,0.0001066734,0.0001591098,0.00007786717],"domain_scores_gemma":[0.999818,0.0000229479,0.00008435527,0.00003303408,0.00002524308,0.00001644098],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002937584,0.000003102716,0.9766507,0.0001036705,0.00002443889,1.392034e-7,0.002510783,0.01184434,0.008338762,0.000421487,0.000007485095,0.00006571576],"study_design_scores_gemma":[0.000288348,0.00001614176,0.9451187,0.0001175917,0.00001360719,4.373223e-7,0.0001122542,0.04875614,0.004665684,0.0008222676,0.00002954761,0.00005926878],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993216,0.00000585683,0.000006223889,0.0003073746,0.0000668663,0.0001228113,0.0001061826,0.000007382501,0.00005564437],"genre_scores_gemma":[0.9998755,0.0000171736,0.00004250978,0.000008537297,0.000008740001,0.000008981515,0.000005864293,0.000003849568,0.000028846],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0369118,"threshold_uncertainty_score":0.1605187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01859677967267018,"score_gpt":0.2071030329113147,"score_spread":0.1885062532386445,"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."}}