{"id":"W4410777901","doi":"10.3390/rs17111860","title":"In-Season Potato Nitrogen Prediction Using Multispectral Drone Data and Machine Learning","year":2025,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Mitacs","keywords":"Drone; Multispectral image; Environmental science; Remote sensing; Computer science; Meteorology; Artificial intelligence; Geography; Botany","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":[0.0004563601,0.0004638086,0.0003060788,0.0004807869,0.0001648356,0.0004269247,0.0004530477,0.0003724363,0.0003110514],"category_scores_gemma":[0.0008187902,0.0002063146,0.0003729773,0.0004444223,0.0001168109,0.0004164332,0.0001797184,0.0003011183,0.0001734257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005281405,"about_ca_system_score_gemma":0.0003995739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04383223,"about_ca_topic_score_gemma":0.07230442,"domain_scores_codex":[0.999878,0.00002707166,0.000005607862,0.00004868305,0.00002358237,0.00001696025],"domain_scores_gemma":[0.9997062,0.0001184098,0.00004870844,0.00002446868,0.00008709775,0.00001505122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0006528628,0.0006065086,0.2251445,0.0002068614,0.0003258499,0.0001847691,0.0001741585,0.5372271,0.04771338,0.0003209468,0.001463115,0.1859799],"study_design_scores_gemma":[0.000009625055,0.00005292418,0.04305849,0.000008090297,0.0000194627,0.0000166896,0.00004319162,0.9532279,0.003131324,0.0000916996,0.0003272281,0.00001325907],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9772446,0.0002516901,0.02045294,0.00008377968,0.00001683079,0.00002081618,0.0005605208,0.0003277971,0.001041146],"genre_scores_gemma":[0.9862824,0.00009131315,0.01235363,0.00002139043,0.000006028738,0.00001333388,0.0008446395,0.00001270594,0.0003745644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04383223,"threshold_uncertainty_score":0.08715421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01653909628059703,"score_gpt":0.2476122556826402,"score_spread":0.2310731594020431,"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."}}