{"id":"W3205952485","doi":"10.3390/rs13204152","title":"Within-Field Yield Prediction in Cereal Crops Using LiDAR-Derived Topographic Attributes with Geographically Weighted Regression Models","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Agriculture and Agri-Food Canada; Canada First Research Excellence Fund","keywords":"Topographic Wetness Index; Lidar; Scale (ratio); Environmental science; Yield (engineering); Elevation (ballistics); Remote sensing; Correlation coefficient; Regression; Digital elevation model; Geography; Cartography; Statistics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002573657,0.0002636031,0.0002891672,0.0001435507,0.0003573018,0.0001028386,0.0001091058,0.0002344052,0.00004908753],"category_scores_gemma":[0.00006388449,0.000227075,0.00009820992,0.001376307,0.000175966,0.0002416057,0.000124851,0.0004662366,0.00001181928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001215937,"about_ca_system_score_gemma":0.00005151837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002791291,"about_ca_topic_score_gemma":0.001689179,"domain_scores_codex":[0.9978784,0.0001383994,0.0004108463,0.0006748287,0.0004624295,0.0004350405],"domain_scores_gemma":[0.9989255,0.0001399297,0.0001651302,0.0005475544,0.00006401659,0.0001578813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002093649,0.0001395833,0.01266898,0.00003524514,0.00006361429,0.0004195827,0.001537282,0.04143154,0.8178422,0.00007989478,0.000159,0.1254137],"study_design_scores_gemma":[0.000525488,0.00008839865,0.009206344,0.0007402466,0.00006028627,0.0004647263,0.0003391568,0.9208407,0.06403088,0.003142247,0.000158253,0.0004032129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9016331,0.00005799317,0.09328532,0.0006796981,0.0001043623,0.0002024764,0.000003309527,0.0001268417,0.003906869],"genre_scores_gemma":[0.9158892,0.00004708104,0.08361246,0.0002323631,0.00006711227,3.327589e-8,0.00002070237,0.00003468286,0.00009637704],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8794092,"threshold_uncertainty_score":0.9259849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02013256060766457,"score_gpt":0.2337749739031241,"score_spread":0.2136424132954595,"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."}}