{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002865113,0.0009119419,0.000382281,0.001231044,0.0002643976,0.000841226,0.0003335859,0.0007004024,0.0004251688],"category_scores_gemma":[0.005968018,0.0002503614,0.0004269023,0.0009385555,0.000176557,0.00100603,0.0004004735,0.0005415138,0.0001165487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005877346,"about_ca_system_score_gemma":0.0005443194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009966175,"about_ca_topic_score_gemma":0.01043182,"domain_scores_codex":[0.9993205,0.0002923018,0.00007367078,0.0001617728,0.0001105587,0.0000412796],"domain_scores_gemma":[0.9969176,0.002264272,0.0002811512,0.0001397713,0.000337211,0.00006006069],"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":[0.0006885857,0.0006533104,0.183786,0.0001516528,0.0007537549,0.0001459054,0.0000671931,0.6973891,0.00666058,0.0004929807,0.0005182525,0.1086927],"study_design_scores_gemma":[0.00001392433,0.0001141279,0.02971926,0.00001647373,0.00004440858,0.0000130215,0.00003432961,0.9682934,0.001390832,0.0002132777,0.0001331995,0.0000136664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9776243,0.0003214402,0.0200373,0.0001062098,0.00003755779,0.0000414943,0.0003576947,0.0001369508,0.001337141],"genre_scores_gemma":[0.9884594,0.0001289394,0.01026297,0.00002938128,0.00001675184,0.00003237898,0.0006438544,0.00001040246,0.000416001],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009966175,"threshold_uncertainty_score":0.01981634,"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."}}