{"id":"W3047934553","doi":"10.1371/journal.pone.0230888","title":"Site-specific machine learning predictive fertilization models for potato crops in Eastern Canada","year":2020,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Potato Plant Research","field":"Agricultural and Biological Sciences","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; Université Laval","funders":"","keywords":"Soil water; Nutrient management; Yield (engineering); Artificial neural network; Mathematics; Random forest; Predictive modelling; Fertilizer; Agricultural soil science; Machine learning; Agronomy; Agricultural engineering; Nutrient; Environmental science; Statistics; Computer science; Soil science; Biology; Soil fertility; Ecology; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0007548612,0.0006614364,0.0005345856,0.0005873562,0.0008085712,0.0008868462,0.001351807,0.0005871117,0.001423232],"category_scores_gemma":[0.00160709,0.0003396372,0.000604765,0.0008333939,0.0005169469,0.0003094223,0.0003962662,0.0006169754,0.0001705975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01603491,"about_ca_system_score_gemma":0.009582927,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9778895,"about_ca_topic_score_gemma":0.9735543,"domain_scores_codex":[0.9997908,0.0000357158,0.00001170254,0.00007109836,0.00003320518,0.0000573953],"domain_scores_gemma":[0.9991486,0.0003166499,0.00006955398,0.00002578398,0.0004007096,0.00003871875],"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":[0.0000876309,0.00005644887,0.03196692,0.00004140954,0.00005901219,0.00006762485,0.00005718856,0.9533389,0.000654379,0.0007975903,0.001108856,0.01176404],"study_design_scores_gemma":[0.000008750676,0.0000104128,0.01011461,0.000004986181,0.00001126915,0.00000548209,0.00004471905,0.989071,0.0001435509,0.0002063944,0.0003686833,0.00001009812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9818661,0.0005439473,0.01237313,0.0002950859,0.00001624805,0.00006774367,0.001794402,0.0002330734,0.002810338],"genre_scores_gemma":[0.9912894,0.0001731489,0.004385683,0.00003660746,0.000003558481,0.00003437795,0.001321889,0.00001879597,0.002736544],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02211052,"threshold_uncertainty_score":0.1163419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1049088351296703,"score_gpt":0.2158712581966908,"score_spread":0.1109624230670205,"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."}}