{"id":"W3175692690","doi":"10.1016/j.heliyon.2021.e07439","title":"Assessing the effectiveness of ground truth data to capture landscape variability from an agricultural region using Gaussian simulation and geostatistical techniques","year":2021,"lang":"en","type":"article","venue":"Heliyon","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"National Institute of Food and Agriculture; Indian Space Research Organisation; U.S. Department of Agriculture; National Aeronautics and Space Administration","keywords":"Ground truth; Agriculture; Geostatistics; Precision agriculture; Variogram; Gaussian; Kriging; Environmental science; Geography; Spatial variability; Computer science; Statistics; Mathematics; Machine learning; Archaeology; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.005820542,0.0005568874,0.0004628215,0.001067462,0.0003104699,0.0008915802,0.0009111727,0.0007262048,0.0002782214],"category_scores_gemma":[0.01966772,0.00027459,0.0007998777,0.00101632,0.0006957617,0.001415253,0.0005932782,0.0005349016,0.00010244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006306355,"about_ca_system_score_gemma":0.0007683079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007724086,"about_ca_topic_score_gemma":0.005318649,"domain_scores_codex":[0.997979,0.001126755,0.0001343073,0.0003529615,0.0003212593,0.00008584183],"domain_scores_gemma":[0.9857679,0.01019273,0.0009738285,0.002160246,0.0008038902,0.0001013374],"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":[0.0001174696,0.0001109762,0.07174084,0.00005426565,0.0001591216,0.00007303419,0.0001341126,0.9002571,0.002935924,0.002694799,0.0001518344,0.02157048],"study_design_scores_gemma":[0.00001217137,0.00006220245,0.01350664,0.00001270797,0.00001869898,0.00003974341,0.00007105518,0.9815487,0.002453303,0.002014742,0.0002415515,0.00001836728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7076822,0.0001367048,0.290305,0.0001374021,0.00001685901,0.00008694134,0.0004742077,0.0004041381,0.000756602],"genre_scores_gemma":[0.9516474,0.00004909731,0.04741019,0.00003069845,0.00000566433,0.00005499366,0.000718475,0.00002562171,0.00005786897],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007724086,"threshold_uncertainty_score":0.03078234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04266409726205082,"score_gpt":0.3293071273368151,"score_spread":0.2866430300747642,"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."}}