{"id":"W2898609277","doi":"10.1007/s10668-018-0283-z","title":"Remote sensing and GIS-based analysis of urban dynamics and modelling of its drivers, the case of Pingtan, China","year":2018,"lang":"en","type":"article","venue":"Environment Development and Sustainability","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; Agriculture and Agri-Food Canada","funders":"Erasmus+","keywords":"Geography; Urban planning; Urbanization; Sustainability; Land use; Population; Driving factors; China; Urban expansion; Socioeconomic status; Economic geography; Physical geography; Environmental resource management; Environmental planning; Environmental science; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006110564,0.0001299949,0.0002781689,0.00006056034,0.0001818436,0.000009202717,0.00006792969,0.00005190896,0.00005032727],"category_scores_gemma":[0.00001350578,0.00009315609,0.00003554116,0.0001868636,0.000255275,0.00008137383,0.000186303,0.00004816057,2.878458e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001403364,"about_ca_system_score_gemma":0.00001666917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008728052,"about_ca_topic_score_gemma":0.0008874401,"domain_scores_codex":[0.9989804,0.00006958631,0.0003482884,0.0002822173,0.0001535348,0.0001659841],"domain_scores_gemma":[0.9994596,0.00006094014,0.0001837202,0.0002211722,0.00001429226,0.00006023369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009232721,0.0000942129,0.8947828,0.0006598993,0.0003400396,0.00002992295,0.01797716,0.02516153,0.0001250654,0.00006477746,0.000003897579,0.06066836],"study_design_scores_gemma":[0.0002182791,0.00005147826,0.1793167,0.00001792564,0.0001919597,0.000004849099,0.001958027,0.8166888,0.00112053,0.0002194266,0.00008841172,0.0001236979],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9865661,0.0001030822,0.01285635,0.000136715,0.000008949612,0.0002246373,0.00000694886,0.000004062904,0.00009319851],"genre_scores_gemma":[0.9974079,0.00005898836,0.002485721,0.00001061718,0.000003768869,5.534856e-7,0.000005489665,0.000005011191,0.00002193319],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7915272,"threshold_uncertainty_score":0.3798795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007436620874841912,"score_gpt":0.1973112644798021,"score_spread":0.1898746436049601,"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."}}