{"id":"W4307475982","doi":"10.1007/s10708-022-10776-4","title":"Modelling global urban land-use change process using spherical cellular automata","year":2022,"lang":"en","type":"article","venue":"GeoJournal","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Urbanization; Cellular automaton; Environmental change; Land use, land-use change and forestry; Land use; Process (computing); Environmental resource management; Global change; Environmental planning; Scale (ratio); Geography; Environmental science; Economic geography; Climate change; Natural resource economics; Physical geography; Computer science; Civil engineering; Economic growth; Economics; Geology; Cartography; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0002806631,0.0005484262,0.0007253319,0.000554555,0.0005325932,0.001051217,0.0009968037,0.001385527,0.001936643],"category_scores_gemma":[0.001596039,0.0005766492,0.001077704,0.0007816431,0.000844572,0.0008112874,0.0007828659,0.000677828,0.0002574324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001334381,"about_ca_system_score_gemma":0.0009314886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08010498,"about_ca_topic_score_gemma":0.03997997,"domain_scores_codex":[0.9998603,0.00004209899,0.000008863622,0.00003445539,0.00002194732,0.00003234254],"domain_scores_gemma":[0.9992487,0.0004681863,0.00006801842,0.00005086305,0.0001046028,0.00005958136],"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":[0.000007451871,0.000006115833,0.0007254215,0.000004795028,0.000008439567,0.0000212194,0.00001506879,0.9973634,0.0001920155,0.001068079,0.00005780795,0.0005302012],"study_design_scores_gemma":[0.000001772639,0.000001971405,0.0001014508,5.127615e-7,0.000001873716,0.000002124073,0.000005277295,0.9994841,0.00003031844,0.0003265844,0.00004239105,0.000001638538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7922763,0.0003689192,0.1935363,0.0005645356,0.0002019689,0.00005460731,0.0007529886,0.000493031,0.01175137],"genre_scores_gemma":[0.993093,0.00008493085,0.005062706,0.00002425425,0.00001293114,0.00002173283,0.0001250506,0.00003144816,0.001543993],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08010498,"threshold_uncertainty_score":0.1592774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04533798881714267,"score_gpt":0.2405478635708228,"score_spread":0.1952098747536802,"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."}}