{"id":"W2800567361","doi":"10.5194/isprs-archives-xlii-3-2219-2018","title":"URBAN EXPANSION MODELING APPROACH BASED ON MULTI-AGENT SYSTEM AND CELLULAR AUTOMATA","year":2018,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Impervious surface; Urban expansion; Cellular automaton; Adaptability; Environmental science; Urban agglomeration; Process (computing); Vegetation (pathology); Land use, land-use change and forestry; Land use; Civil engineering; Computer science; Geography; Ecology; Engineering; Economic geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000264319,0.0006632898,0.000548191,0.000796791,0.0007600956,0.0009493718,0.001031924,0.000799749,0.002165575],"category_scores_gemma":[0.0006295788,0.0003529749,0.0009409021,0.000578469,0.000448024,0.0009520375,0.000731296,0.0005546839,0.000211584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001072616,"about_ca_system_score_gemma":0.0008455153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03729137,"about_ca_topic_score_gemma":0.02268879,"domain_scores_codex":[0.9998114,0.00006416347,0.00001027125,0.00004795179,0.00003803805,0.00002829029],"domain_scores_gemma":[0.9997249,0.0001276351,0.00003845203,0.00001387903,0.00007055514,0.00002451104],"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":[0.00001184913,0.00001374578,0.001056835,0.00001992463,0.00002229268,0.00007222746,0.00003837475,0.9916202,0.0004382545,0.003698515,0.0002004152,0.002807344],"study_design_scores_gemma":[0.000001670307,0.000004409088,0.00009169269,0.000001490309,0.000005031674,0.000005211079,0.000007200539,0.9992257,0.00004552896,0.000417006,0.0001927658,0.000002166285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.246316,0.0009660788,0.7230169,0.0006549143,0.0001885326,0.0001652002,0.000377129,0.0007596385,0.02755561],"genre_scores_gemma":[0.9719898,0.0003060121,0.02334927,0.00003474648,0.00002440974,0.0001446966,0.0001057547,0.00002323097,0.004022126],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03729137,"threshold_uncertainty_score":0.07414865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01937091176640392,"score_gpt":0.2349271816628669,"score_spread":0.215556269896463,"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."}}