{"id":"W3039045927","doi":"10.3390/rs12132071","title":"Using Linear Regression, Random Forests, and Support Vector Machine with Unmanned Aerial Vehicle Multispectral Images to Predict Canopy Nitrogen Weight in Corn","year":2020,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":151,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multispectral image; Canopy; Random forest; Mean squared error; Support vector machine; Environmental science; Linear regression; Remote sensing; Computer science; Mathematics; Statistics; Machine learning; Geography","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":[0.0008389381,0.0005973257,0.0002779374,0.0006937915,0.0001865825,0.0004580194,0.0003472023,0.0002953705,0.0002267118],"category_scores_gemma":[0.001314191,0.0001876093,0.0004981901,0.0004466955,0.0001441383,0.0003611505,0.000161951,0.0002551266,0.0001046581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003813457,"about_ca_system_score_gemma":0.0005435894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06172082,"about_ca_topic_score_gemma":0.04729059,"domain_scores_codex":[0.999797,0.00005372247,0.00001651506,0.0000532703,0.00004814301,0.00003124892],"domain_scores_gemma":[0.9995818,0.0002048447,0.0000662782,0.00002115484,0.0001114898,0.00001445849],"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":[0.000327238,0.0002309911,0.05845326,0.0001188383,0.0001701328,0.0001604689,0.00009715405,0.7439207,0.01668254,0.0002388929,0.0005587717,0.179041],"study_design_scores_gemma":[0.000004995791,0.000046349,0.01001126,0.000004308991,0.0000157312,0.00001317366,0.00003095065,0.9871903,0.002511559,0.00006274866,0.00009870687,0.000009975678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9486601,0.0003542546,0.04960518,0.00005708668,0.00002134981,0.00003718813,0.0001794873,0.0004826849,0.0006026914],"genre_scores_gemma":[0.9759999,0.00009520786,0.02315696,0.0000112306,0.000005297325,0.00001818829,0.0002425452,0.00001259709,0.0004580497],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06172082,"threshold_uncertainty_score":0.1227231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01088598802926091,"score_gpt":0.2291034954844696,"score_spread":0.2182175074552087,"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."}}