{"id":"W3042701400","doi":"10.3390/rs12142230","title":"Using Artificial Neural Networks and Remotely Sensed Data to Evaluate the Relative Importance of Variables for Prediction of Within-Field Corn and Soybean Yields","year":2020,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; Concordia University","funders":"Agriculture and Agri-Food Canada","keywords":"Normalized Difference Vegetation Index; Crop; Yield (engineering); Growing season; Crop yield; Vegetation (pathology); Mathematics; Artificial neural network; Environmental science; Statistics; Agricultural engineering; Agronomy; Machine learning; Leaf area index; Computer science; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005589427,0.0001644899,0.0002616027,0.00001654333,0.0001752706,0.00003230785,0.0001287016,0.0001264978,0.000002164126],"category_scores_gemma":[0.0007852661,0.0001187847,0.00003316488,0.0002880568,0.000177645,0.0001788112,0.0002870495,0.0002153939,3.010617e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003080019,"about_ca_system_score_gemma":0.00001306643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002573748,"about_ca_topic_score_gemma":0.0001370283,"domain_scores_codex":[0.998519,0.0001260455,0.0004238373,0.0004607383,0.0002628897,0.0002074589],"domain_scores_gemma":[0.9988674,0.0003740148,0.0002481608,0.0003666828,0.00004454625,0.00009920008],"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":[0.0005087508,0.0000131866,0.001298716,0.000060599,0.0000828327,0.0000127476,0.004135976,0.1230594,0.8267888,0.00008520269,0.0009140214,0.04303968],"study_design_scores_gemma":[0.0001742102,0.0001420982,0.002521176,0.00007682029,0.000107728,0.00005872449,0.0002760328,0.989277,0.006551378,0.0006734113,0.00002764935,0.0001137364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8040456,0.00004564659,0.1937667,0.001302751,0.0001482138,0.0004926486,0.00002115926,0.00002156966,0.0001556507],"genre_scores_gemma":[0.912556,0.000006464599,0.0867819,0.0004755629,0.0001448704,4.845487e-9,0.00001284837,0.00001682341,0.000005551707],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8662176,"threshold_uncertainty_score":0.4843899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08654463905637318,"score_gpt":0.2814216753334,"score_spread":0.1948770362770268,"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."}}