{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003618069,0.00005757682,0.00006200344,0.00003504572,0.0008441407,0.0005850641,0.0001939739,0.00002174959,0.000007776788],"category_scores_gemma":[0.00002228055,0.00004465229,0.00001008841,0.00005276665,0.00009202163,0.005826722,0.0002438062,0.00001964424,0.00003080324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001548466,"about_ca_system_score_gemma":0.000005958459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001067773,"about_ca_topic_score_gemma":0.000006884533,"domain_scores_codex":[0.9994645,0.000004161941,0.0001286313,0.0001193305,0.0001570712,0.0001263238],"domain_scores_gemma":[0.999575,0.00002089303,0.0001138592,0.0001945537,0.00002549493,0.00007024807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003072368,0.00001353774,0.04492165,0.0002559396,0.000008503797,0.000001213557,0.007168073,0.006736163,0.0007894672,0.001114805,0.001673498,0.9372864],"study_design_scores_gemma":[0.0001963932,0.00002755595,0.06671415,0.00001244499,0.000002303926,0.000002092246,0.000009076381,0.9255801,0.0003596819,0.0002673403,0.006759559,0.00006926685],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7843888,0.000004522357,0.2121246,0.000199315,0.0001910903,0.0001584086,0.000003118168,0.0000277233,0.002902427],"genre_scores_gemma":[0.9912639,0.000007547223,0.00842389,0.0002548387,0.00003806103,2.406431e-7,0.000003130599,0.000001383272,0.000006990593],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9372172,"threshold_uncertainty_score":0.6492532,"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."}}