{"id":"W126684057","doi":"","title":"Dynamic simulation of land-use changes in a periurban agricultural system","year":2005,"lang":"en","type":"book-chapter","venue":"Agritrop (Cirad)","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Fund for Agricultural Development; Consortium of International Agricultural Research Centers; Centre de Coopération Internationale en Recherche Agronomique pour le Développement; Asian Institute of Technology; European Commission; International Development Research Centre","keywords":"Cellular automaton; Geographic information system; Geography; Fractal; Cartography; Process (computing); Land use; Land use, land-use change and forestry; Simulation modeling; Fractal dimension; Spatial analysis; Agricultural land; Computer science; Remote sensing; Mathematics; Civil engineering; Algorithm; Engineering","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":[0.0002311743,0.0003342598,0.0003375666,0.0003879914,0.0004397857,0.0007198776,0.0006745041,0.0008289298,0.003041907],"category_scores_gemma":[0.0007693893,0.0002726592,0.0004041982,0.0005949311,0.0005586104,0.0006720652,0.0005424374,0.0004566823,0.0003045526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001534953,"about_ca_system_score_gemma":0.0007096768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04795576,"about_ca_topic_score_gemma":0.02936636,"domain_scores_codex":[0.9998806,0.0000506013,0.000006257738,0.00002235307,0.00001554759,0.00002481052],"domain_scores_gemma":[0.9995927,0.0002400705,0.00003958805,0.00002302659,0.00004247883,0.0000622279],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001733077,0.00001719882,0.002785929,0.000006386132,0.000009932683,0.0000518179,0.00003136707,0.9953305,0.0001590731,0.0007048827,0.0001158847,0.0007698255],"study_design_scores_gemma":[0.00001030632,0.00001395459,0.0008256285,0.000002262784,0.000003995657,0.00001004154,0.00004086093,0.9982497,0.00009472152,0.0004331844,0.0003112387,0.000004016968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9712001,0.0001100258,0.01685556,0.0002358222,0.00001756731,0.00003507056,0.0009279702,0.0002197763,0.01039809],"genre_scores_gemma":[0.9941229,0.0000816504,0.003001621,0.00001882314,0.000003805625,0.00004637128,0.0003824508,0.00001874388,0.002323776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04795576,"threshold_uncertainty_score":0.09535325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009951469052651922,"score_gpt":0.1922121298465474,"score_spread":0.1822606607938954,"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."}}