{"id":"W4407737422","doi":"10.1109/whispers65427.2024.10876484","title":"Multispectral Drone Imaging for Non-Destructive Estimation of Nitrogen Content in Canola and Wheat","year":2024,"lang":"en","type":"article","venue":"","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"University of Lethbridge","keywords":"Canola; Multispectral image; Drone; Nitrogen; Environmental science; Remote sensing; Computer science; Agronomy; Artificial intelligence; Chemistry; Biology; Botany; Geography","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":[0.0002762003,0.0004089867,0.0002060033,0.0005788137,0.0001715426,0.0003167164,0.0002650266,0.0002753221,0.00038472],"category_scores_gemma":[0.0003088167,0.0001577038,0.0001985773,0.0003564369,0.0001170535,0.0003399182,0.0001953283,0.0002265102,0.00011672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003543132,"about_ca_system_score_gemma":0.0001839183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008684726,"about_ca_topic_score_gemma":0.02653165,"domain_scores_codex":[0.999876,0.000020281,0.000003809004,0.00004873777,0.00004389197,0.00000721841],"domain_scores_gemma":[0.9998643,0.00005584455,0.00002812098,0.0000140559,0.00002911841,0.000008529156],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001983405,0.0001108567,0.02249452,0.000174824,0.00007860288,0.00008347339,0.0001422733,0.01756401,0.8805088,0.0001724491,0.0002832175,0.07818864],"study_design_scores_gemma":[0.00002484738,0.0002441148,0.1629081,0.0000290788,0.0001053581,0.0002316255,0.0001881638,0.3774317,0.4553358,0.0002729759,0.0031425,0.00008571688],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9194626,0.001141278,0.07609398,0.00007822234,0.00002031164,0.00005440428,0.0003701767,0.0006535606,0.002125551],"genre_scores_gemma":[0.9414874,0.000451866,0.05690144,0.00005168576,0.000005619655,0.00005085098,0.0002482976,0.00003962628,0.0007633035],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008684726,"threshold_uncertainty_score":0.01726836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01633276095045364,"score_gpt":0.2868032710154109,"score_spread":0.2704705100649572,"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."}}