{"id":"W2058553647","doi":"10.3808/jei.200300016","title":"Detecting Urban Land-Use and Land-Cover Changes in Mississauga Using Landsat TM Images","year":2003,"lang":"en","type":"article","venue":"Journal of Environmental Informatics","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Impervious surface; Land cover; Land use; Remote sensing; Vegetation (pathology); Geography; Environmental science; Physical geography; Hydrology (agriculture); Forestry; Geology; Ecology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.0004096111,0.000140407,0.0002198265,0.00007217078,0.00008397968,0.00008423535,0.0001051655,0.00006847565,0.0004696377],"category_scores_gemma":[0.00002339078,0.0001033584,0.00004067309,0.00007508452,0.00002489873,0.001036631,0.0000788227,0.0001621639,0.00003411319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001354973,"about_ca_system_score_gemma":0.000004742623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006773655,"about_ca_topic_score_gemma":0.0001141262,"domain_scores_codex":[0.9989497,0.00004296292,0.0004578072,0.0000656236,0.0002579869,0.0002259348],"domain_scores_gemma":[0.9993637,0.00005977686,0.0003594017,0.0001007894,0.000002029771,0.0001142711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002152999,0.00004396693,0.9937012,0.00003246026,0.00001485675,0.00001989025,0.001189904,0.002720994,0.001772794,2.765132e-7,0.00005442496,0.0004276989],"study_design_scores_gemma":[0.004324401,0.0005251595,0.9248763,0.0004360869,0.0001574425,0.001982177,0.003737848,0.01994661,0.01828141,0.0001086951,0.02472394,0.000899889],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990855,0.0001597484,0.0000404877,0.00002205557,0.00007968547,0.00007703055,0.00001131773,0.000003924738,0.0005202564],"genre_scores_gemma":[0.9975719,0.0003174908,0.001872468,0.0001663872,0.00003423917,6.733778e-7,0.000001510764,0.00001028838,0.00002506105],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06882486,"threshold_uncertainty_score":0.5142203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0145633816466752,"score_gpt":0.2081300915254834,"score_spread":0.1935667098788082,"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."}}