{"id":"W4412123868","doi":"10.13031/aim.202500616","title":"Identifying yellow hawkweed in wild blueberry fields using drone images for site-specific herbicide application","year":2025,"lang":"en","type":"article","venue":"","topic":"Rangeland and Wildlife Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Drone; Computer science; Computer vision; Artificial intelligence; Remote sensing; Biology; Botany; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009637451,0.0003462365,0.0001864086,0.0005856828,0.0001006863,0.0002344948,0.0002190961,0.0002598002,0.0008790156],"category_scores_gemma":[0.0002101232,0.00009344709,0.0002269464,0.0001435915,0.00005915521,0.0001935405,0.0001378171,0.0001585674,0.0002584723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002189645,"about_ca_system_score_gemma":0.0001461611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01186373,"about_ca_topic_score_gemma":0.03281019,"domain_scores_codex":[0.9999486,0.000004643206,0.000001923538,0.00001882754,0.00001452056,0.00001141738],"domain_scores_gemma":[0.9999365,0.00001577801,0.00001003301,0.000007207022,0.00002330763,0.000007187611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007469436,0.0003508896,0.08673853,0.0004590171,0.0002395963,0.001068776,0.0003209135,0.06216237,0.3822355,0.0003292021,0.005458379,0.4598898],"study_design_scores_gemma":[0.00002576971,0.0003056117,0.1854768,0.00006259893,0.0001096663,0.0005923779,0.0005391096,0.7311081,0.07648127,0.0002197506,0.005040052,0.00003894961],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9614831,0.0005790366,0.0316461,0.0000916224,0.00005355409,0.00009083399,0.001077808,0.001405583,0.003572416],"genre_scores_gemma":[0.9728965,0.0003163037,0.02375725,0.00005879837,0.00001013823,0.00002334492,0.001133262,0.00003291214,0.001771532],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01186373,"threshold_uncertainty_score":0.02358937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01630507334477066,"score_gpt":0.2660661863844502,"score_spread":0.2497611130396795,"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."}}