{"id":"W2766927973","doi":"10.3390/rs9101057","title":"A New Method to Map Groundwater Table in Peatlands Using Unmanned Aerial Vehicles","year":2017,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Peatlands and Wetlands Ecology","field":"Environmental Science","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Calgary","funders":"University of Waterloo; Emissions Reduction Alberta; Shell Canada","keywords":"Peat; Water table; Groundwater; Environmental science; Terrain; Remote sensing; Hydrology (agriculture); Table (database); Photogrammetry; Geology; Computer science; Cartography; Geography; Data mining","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":[0.0004235579,0.0001552211,0.0002517313,0.00005851407,0.0003519393,0.0001753678,0.0002174516,0.0001050764,0.0001450834],"category_scores_gemma":[0.00004465753,0.0001385345,0.0000433092,0.0000822403,0.00004163877,0.0002060652,0.0003492967,0.0001205559,0.0001077831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001870127,"about_ca_system_score_gemma":0.00002148198,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01990249,"about_ca_topic_score_gemma":0.004520535,"domain_scores_codex":[0.9987071,0.0000804412,0.0001999836,0.0003757091,0.0001531512,0.0004836224],"domain_scores_gemma":[0.9993032,0.00003356401,0.000087455,0.0004048837,0.000005534837,0.0001653677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003993314,0.0000461042,0.04559051,0.0000203419,0.00003374551,0.0006897436,0.001738015,0.009499362,0.4070616,0.00002302292,0.005520571,0.5293777],"study_design_scores_gemma":[0.005190248,0.0003944233,0.1742804,0.0002968129,0.00008516908,0.000568727,0.0001810532,0.6865765,0.0220105,0.00645919,0.1024168,0.001540198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9285504,0.000006850586,0.06545926,0.0009744926,0.000348911,0.0001557077,8.467138e-7,0.00002668884,0.004476877],"genre_scores_gemma":[0.7968244,0.000002661645,0.2011116,0.0002586535,0.0003156997,1.75988e-8,0.000005171535,0.00002031079,0.001461481],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6770772,"threshold_uncertainty_score":0.9866241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02348556159997361,"score_gpt":0.2954474313859914,"score_spread":0.2719618697860178,"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."}}