{"id":"W4396667465","doi":"10.3390/rs16091648","title":"Mapping the Continuous Cover of Invasive Noxious Weed Species Using Sentinel-2 Imagery and a Novel Convolutional Neural Regression Network","year":2024,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"National Key Research and Development Program of China; China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Remote sensing; Noxious weed; Grassland; Random forest; Convolutional neural network; Environmental science; Weed; Physical geography; Computer science; Ecology; Geography; Artificial intelligence; Biology","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":[0.000491064,0.0008463544,0.0002502796,0.0006782632,0.0001253774,0.0002485825,0.0005344962,0.0003145654,0.0004453462],"category_scores_gemma":[0.0005453681,0.0002221027,0.0004576294,0.0003305442,0.0001315953,0.0004067954,0.0002913606,0.0002963072,0.000169997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003750081,"about_ca_system_score_gemma":0.0003550407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01882012,"about_ca_topic_score_gemma":0.02481952,"domain_scores_codex":[0.9998349,0.0000228668,0.000006375025,0.00006067977,0.00004167971,0.00003350621],"domain_scores_gemma":[0.9998671,0.00003299435,0.00003091818,0.00001177138,0.00004669508,0.0000105774],"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":[0.0005835017,0.0004583477,0.09492411,0.0002246132,0.0004256832,0.0005785861,0.0001257748,0.5369755,0.1091209,0.0007561461,0.003901266,0.2519255],"study_design_scores_gemma":[0.000004956802,0.0000303458,0.008965348,0.000004835176,0.00002242416,0.00003108431,0.00001351625,0.987749,0.00286437,0.00006772189,0.0002382783,0.000007955347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8921657,0.000532472,0.1023624,0.0002004943,0.00006733859,0.0000571231,0.001080361,0.00127534,0.002258746],"genre_scores_gemma":[0.9555631,0.000205945,0.04103911,0.00006525316,0.00002559584,0.00003244363,0.001710921,0.00003282915,0.001324646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01882012,"threshold_uncertainty_score":0.03742111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02326923762434986,"score_gpt":0.2221224474734503,"score_spread":0.1988532098491004,"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."}}