{"id":"W3005985007","doi":"10.1109/jstars.2020.2973490","title":"Model Sensitivity to Topographic Uncertainty in Meso- and Microtidal Marshes","year":2020,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Coastal wetland ecosystem dynamics","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"NOAA Research; Louisiana Sea Grant, Louisiana State University; National Science Foundation; University of Central Florida; National Oceanic and Atmospheric Administration; Canadian Centre for Applied Research in Cancer Control","keywords":"Marsh; Lidar; Digital elevation model; Environmental science; Estuary; Wetland; Altimeter; Geology; Bay; Hydrology (agriculture); Remote sensing; Oceanography; Ecology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003328147,0.0001137062,0.0002307168,0.00009429778,0.00006711139,0.00003892072,0.00004427058,0.0000702562,0.000002196765],"category_scores_gemma":[0.00006118666,0.000107355,0.00002188473,0.0006604749,0.00004134021,0.00008066326,0.00005859278,0.0002796228,6.974328e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005624179,"about_ca_system_score_gemma":0.00002952667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003547888,"about_ca_topic_score_gemma":0.0126609,"domain_scores_codex":[0.999051,0.00004480005,0.0003573629,0.0001796979,0.000177874,0.0001893182],"domain_scores_gemma":[0.9996072,0.000056187,0.0001060649,0.00006883361,0.00003574668,0.0001259352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001549852,0.00003760989,0.05050051,0.00006477267,0.00002252244,0.00009088763,0.00283182,0.5652593,0.252962,0.00008713939,0.00007533239,0.1279131],"study_design_scores_gemma":[0.0003874864,0.00003848341,0.1442222,0.00005630547,0.000007895215,0.00005672615,0.0001143787,0.8538358,0.0003891508,0.0005802163,0.0001811225,0.0001301751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9848458,0.00001240775,0.01302805,0.001714499,0.00003897304,0.0001606032,0.000003389306,0.000007600102,0.0001886399],"genre_scores_gemma":[0.9556555,0.00005456871,0.0438076,0.0004096208,0.00005006606,4.637756e-8,0.000001120896,0.000008196017,0.00001326949],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2885765,"threshold_uncertainty_score":0.7065077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02052707573236612,"score_gpt":0.2097221860033374,"score_spread":0.1891951102709712,"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."}}