{"id":"W626894379","doi":"","title":"Analyzing Fuzzy Logic, Logistic-Decision Tree, and Neural Network Classification for Extracting Subzonal Land Uses from Remote Sensing Imagery","year":2012,"lang":"en","type":"article","venue":"Transportation Research Board 91st Annual MeetingTransportation Research Board","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Decision tree; Land cover; Data mining; Geographic information system; Artificial neural network; Remote sensing; Artificial intelligence; Land use; Geography; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.007280014,0.000408862,0.0005300727,0.0003869452,0.001632353,0.0003201326,0.0004247905,0.0003381087,0.0003444936],"category_scores_gemma":[0.0004827337,0.0003711045,0.0001844001,0.001290593,0.0003435207,0.001711562,0.00003740452,0.00100406,0.0001032145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002165614,"about_ca_system_score_gemma":0.00007437835,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02061992,"about_ca_topic_score_gemma":0.0567545,"domain_scores_codex":[0.9927302,0.0008012534,0.001147219,0.001093339,0.002281297,0.001946715],"domain_scores_gemma":[0.9939567,0.003900493,0.0003196741,0.0004720436,0.00065068,0.0007003455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001639702,0.0001453278,0.9253856,0.0002546523,0.00008620509,0.00004648636,0.003694856,0.005435956,0.004914948,0.000249066,0.001838267,0.05630896],"study_design_scores_gemma":[0.001131549,0.0002082798,0.9639882,0.0002666803,0.00007484775,0.000001271972,0.003126135,0.02348507,0.000355961,0.004165717,0.002717345,0.0004788825],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9827448,0.0006023291,0.01319639,0.0007809155,0.0002922876,0.001300611,0.0003061368,0.0001424721,0.0006340913],"genre_scores_gemma":[0.9735384,0.0004366885,0.02418443,0.00007098066,0.0007843799,0.00004840772,0.0007748269,0.00008064628,0.00008126779],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05583008,"threshold_uncertainty_score":0.9998741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1084536343503065,"score_gpt":0.3735836691479232,"score_spread":0.2651300347976168,"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."}}