{"id":"W2067438610","doi":"10.1016/j.isprsjprs.2014.05.001","title":"Topographic and spectral data resolve land cover misclassification to distinguish and monitor wetlands in western Uganda","year":2014,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; National Science Foundation","keywords":"Wetland; Land cover; Geography; Threatened species; Land use; Wildlife; Agroforestry; National park; Agricultural land; Habitat; Environmental science; Agriculture; Remote sensing; Ecology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006640474,0.0001141394,0.0002192857,0.0001004538,0.00009981735,0.0001285298,0.0001089275,0.00006249531,0.000005243621],"category_scores_gemma":[0.00006046257,0.00008360347,0.00001929458,0.000189225,0.00003808496,0.0001833273,0.0001298873,0.0001502151,0.000001810099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001533641,"about_ca_system_score_gemma":0.000003435656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001789867,"about_ca_topic_score_gemma":0.006598959,"domain_scores_codex":[0.9990755,0.00006081319,0.0002798542,0.0002293746,0.0001651806,0.0001892561],"domain_scores_gemma":[0.9993997,0.0000897201,0.0001488189,0.0001761061,0.00001202673,0.0001736944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001255346,0.00002195263,0.8040508,0.00006461531,0.00002151224,0.00004590676,0.0005984438,0.00003777988,0.006419822,5.538671e-7,0.00005758781,0.1885555],"study_design_scores_gemma":[0.001548586,0.0003096164,0.9229469,0.0005522196,0.00007840214,0.0007583807,0.0002649195,0.05662498,0.001452049,0.0002981537,0.01483047,0.0003353261],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976266,0.0001563692,0.001475183,0.0002737048,0.0001349732,0.00007800879,0.00000387312,0.000005266186,0.0002460442],"genre_scores_gemma":[0.9965487,0.0002325542,0.002912819,0.0001125189,0.0001696406,1.664042e-8,0.000003225616,0.000008404486,0.00001208709],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1882202,"threshold_uncertainty_score":0.3682374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0160232525628,"score_gpt":0.2483240398636141,"score_spread":0.2323007873008142,"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."}}