{"id":"W2146645857","doi":"10.3390/s7102028","title":"A Wetness Index Using Terrain-Corrected Surface Temperature and Normalized Difference Vegetation Index Derived from Standard MODIS Products: An Evaluation of Its Use in a Humid Forest-Dominated Region of Eastern Canada","year":2007,"lang":"en","type":"article","venue":"Sensors","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Foundation for Climate and Atmospheric Sciences; BIOCAP Canada; National Aeronautics and Space Administration","keywords":"Normalized Difference Vegetation Index; Environmental science; Vegetation (pathology); Elevation (ballistics); Terrain; Topographic Wetness Index; Enhanced vegetation index; Water content; Digital elevation model; Atmospheric sciences; Atmosphere (unit); Spatial distribution; Spatial variability; Leaf area index; Hydrology (agriculture); Remote sensing; Meteorology; Geology; Vegetation Index; Geography; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008586072,0.000514569,0.000257733,0.001689245,0.0007679284,0.001081377,0.0006632206,0.0001939641,0.0007334984],"category_scores_gemma":[0.001437672,0.0002043843,0.0002896084,0.002093368,0.0002656658,0.0003803895,0.0004276751,0.0001625852,0.000139022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004200744,"about_ca_system_score_gemma":0.003622997,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8215058,"about_ca_topic_score_gemma":0.9228855,"domain_scores_codex":[0.9995093,0.0000329115,0.00002850943,0.00007305414,0.0003067711,0.00004941049],"domain_scores_gemma":[0.9991276,0.0001108674,0.0001048892,0.00004621357,0.0005322759,0.00007809774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004863604,0.0001572186,0.7418243,0.0002661428,0.0003502628,0.0001886918,0.0005228679,0.01536914,0.01919499,0.0003824962,0.001275381,0.2199821],"study_design_scores_gemma":[0.00004013674,0.0001128822,0.9149672,0.00002317,0.0001149911,0.0001382014,0.0005002542,0.0733384,0.007806532,0.0001051685,0.002789082,0.00006403759],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9835535,0.0007207372,0.009346912,0.00003705018,0.000009956061,0.000141706,0.002122554,0.0003783132,0.003689176],"genre_scores_gemma":[0.9630441,0.0005899235,0.03205269,0.00002035825,0.000006622809,0.00006562067,0.002280925,0.00006456279,0.00187516],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1784942,"threshold_uncertainty_score":0.3590906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01825686788292636,"score_gpt":0.2314896652132011,"score_spread":0.2132327973302748,"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."}}