{"id":"W3184968177","doi":"10.3390/rs13132555","title":"Using Hybrid Artificial Intelligence and Evolutionary Optimization Algorithms for Estimating Soybean Yield and Fresh Biomass Using Hyperspectral Vegetation Indices","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Grain Farmers of Ontario","keywords":"Hyperspectral imaging; Yield (engineering); Biomass (ecology); Artificial neural network; Ranking (information retrieval); Mathematics; Computer science; Artificial intelligence; Pattern recognition (psychology); Biology; Agronomy; Materials science","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":[0.0008793137,0.00126747,0.0005889146,0.0006564848,0.0002453508,0.0005594677,0.000554827,0.0007143592,0.0004205999],"category_scores_gemma":[0.001245682,0.0004536583,0.0007355427,0.0005220457,0.0002161435,0.000514709,0.0004065562,0.0006185244,0.0001315784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004995467,"about_ca_system_score_gemma":0.0007287251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007937044,"about_ca_topic_score_gemma":0.008708325,"domain_scores_codex":[0.9997289,0.00006094381,0.00002160758,0.00009318434,0.00006321635,0.00003228615],"domain_scores_gemma":[0.9996729,0.000162398,0.00004893954,0.00001711668,0.00008546496,0.00001317565],"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":[0.00006151445,0.00007788846,0.003165158,0.0000454916,0.0001340729,0.00004461354,0.00003702291,0.8954758,0.004803116,0.000714527,0.0002735393,0.09516732],"study_design_scores_gemma":[0.000002240255,0.00001316549,0.0003044702,0.000001660386,0.000005962187,0.000003401891,0.000002256625,0.9990696,0.0003961438,0.0001391711,0.00005943761,0.000002396298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1601357,0.0005933063,0.8356603,0.0001368152,0.00004167287,0.00005868808,0.00009145446,0.0006290775,0.002653071],"genre_scores_gemma":[0.7587247,0.0002562953,0.2379402,0.0001395204,0.00003083504,0.0001664788,0.0002982321,0.00005606843,0.002387633],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007937044,"threshold_uncertainty_score":0.0157817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04950638788076424,"score_gpt":0.2767925534414044,"score_spread":0.2272861655606401,"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."}}