{"id":"W4415138409","doi":"10.3390/agronomy15102384","title":"Evaluating the Performance of Winter Wheat Under Late Sowing Using UAV Multispectral Data","year":2025,"lang":"en","type":"article","venue":"Agronomy","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Agriculture","funders":"National Key Research and Development Program of China; Government of Jiangsu Province; National Natural Science Foundation of China","keywords":"Sowing; Winter wheat; Multispectral image; Vegetation (pathology); Yield (engineering); Growing season","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000482587,0.000488741,0.0002480427,0.0007402449,0.000166465,0.0004645014,0.000194815,0.000271846,0.0001729201],"category_scores_gemma":[0.0007656118,0.0001096033,0.0002637267,0.0004078559,0.0001057534,0.0003872594,0.0001853266,0.0001487007,0.0000938085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000237053,"about_ca_system_score_gemma":0.0001491631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006009528,"about_ca_topic_score_gemma":0.007239749,"domain_scores_codex":[0.9998318,0.00002727071,0.00001277231,0.00005415541,0.00004716204,0.00002678097],"domain_scores_gemma":[0.9997196,0.00008208869,0.00006378683,0.00002840805,0.00008614718,0.00002005175],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008591574,0.000415478,0.4088021,0.0003106974,0.000250123,0.0005177981,0.0004400484,0.1080668,0.2133668,0.0003018173,0.0009955609,0.2656735],"study_design_scores_gemma":[0.0000111764,0.0004844208,0.3409072,0.00001806274,0.00008969715,0.0001934586,0.0002566043,0.6249803,0.03234493,0.0001787194,0.0004954584,0.00003994863],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910287,0.0001362707,0.008267074,0.00001735845,0.000008450366,0.000008147031,0.0001348337,0.00008736301,0.0003117507],"genre_scores_gemma":[0.9963034,0.00006396891,0.003309214,0.000006348801,0.000002630176,0.000003578985,0.0001942423,0.000003757675,0.0001128105],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006009528,"threshold_uncertainty_score":0.01194906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05751196841060659,"score_gpt":0.3241102579457334,"score_spread":0.2665982895351269,"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."}}