{"id":"W2766580866","doi":"10.5539/cis.v10n4p38","title":"Remote Sensing, Gis and Cellular Automata for Urban Growth Simulation","year":2017,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cellular automaton; Computer science; Simple (philosophy); Representation (politics); Land cover; Simplicity; Cover (algebra); Task (project management); Remote sensing; Land use; Artificial intelligence; Civil engineering; Systems engineering; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.0003157414,0.0005348012,0.0004338239,0.0007284762,0.0003192836,0.0006755383,0.0005232859,0.000471595,0.00294274],"category_scores_gemma":[0.001189125,0.0002826772,0.0004197521,0.001079775,0.0005239894,0.0006626601,0.0006199941,0.0005421165,0.0005898168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009188929,"about_ca_system_score_gemma":0.0006076634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02475905,"about_ca_topic_score_gemma":0.01825267,"domain_scores_codex":[0.9998035,0.00007663901,0.00001064749,0.00002659327,0.00007278789,0.000009909102],"domain_scores_gemma":[0.9997221,0.0001583825,0.00003369349,0.00002798952,0.00004306883,0.000014752],"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":[0.00001569204,0.00002259001,0.001306172,0.00006323566,0.00002622739,0.0001240836,0.00004576063,0.9374316,0.001195526,0.02770127,0.002484234,0.02958367],"study_design_scores_gemma":[0.000004659949,0.00001022938,0.000431484,0.00001098734,0.000005584876,0.00003439531,0.00002222529,0.9791849,0.0003184638,0.01426193,0.005705213,0.00001000039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04834835,0.002870901,0.9010912,0.001522865,0.0002746701,0.0002081763,0.001711934,0.003307536,0.04066442],"genre_scores_gemma":[0.6131524,0.002499433,0.3687902,0.0002059806,0.0001432272,0.0003898818,0.001092204,0.0003424706,0.01338423],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02475905,"threshold_uncertainty_score":0.04922986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01260118504957085,"score_gpt":0.233552481920615,"score_spread":0.2209512968710441,"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."}}