{"id":"W2003187416","doi":"10.1007/s00267-004-4067-x","title":"Modeling the Ecological Consequences of Land-Use Policies in an Urbanizing Region","year":2005,"lang":"en","type":"article","venue":"Environmental Management","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"National Oceanic and Atmospheric Administration","keywords":"Zoning; Land use; Urbanization; Wetland; Environmental resource management; Land use, land-use change and forestry; Variety (cybernetics); Environmental planning; Business; Ecology; Geography; Environmental science; Political science; Computer science","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":[0.0001805374,0.0001111972,0.0001146362,0.00002871091,0.00009340302,0.00002849953,0.0002746585,0.00003560362,0.0003727783],"category_scores_gemma":[0.000001272237,0.00007158067,0.000033242,0.00007245866,0.00006698023,0.0003933126,0.0002593494,0.0000624193,0.0001178663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001396245,"about_ca_system_score_gemma":7.618994e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006758067,"about_ca_topic_score_gemma":0.001980453,"domain_scores_codex":[0.9990461,0.00006581251,0.0002397254,0.0002262312,0.0002042599,0.0002178526],"domain_scores_gemma":[0.9996485,0.0000194342,0.00005580767,0.0002346468,3.855365e-7,0.00004119557],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001977985,0.0002437092,0.7101811,0.00001068241,0.00001211434,0.00001608681,0.0006866,0.2852604,0.000675338,0.0001916638,0.00004050056,0.002662033],"study_design_scores_gemma":[0.0005761088,0.000133897,0.8582218,0.0000383505,0.00003292809,0.00001205737,0.001829664,0.1345467,0.000616224,0.0004250873,0.00328107,0.0002861132],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976347,0.00005254965,0.00005403336,0.0005192427,0.000027644,0.0002420828,0.000002284949,0.00001549219,0.001451953],"genre_scores_gemma":[0.9989305,0.0001877034,0.0003160865,0.0004299009,0.00002537594,0.00002324638,0.000005458303,0.000007235143,0.00007446357],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1507138,"threshold_uncertainty_score":0.4081661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02448664100045734,"score_gpt":0.2165501195296482,"score_spread":0.1920634785291909,"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."}}