{"id":"W4410757907","doi":"10.1007/978-3-031-91835-3_19","title":"Robust UDA for Crop and Weed Segmentation: Multi-scale Attention and Style-Adaptive Techniques","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Weed; Scale (ratio); Segmentation; Artificial intelligence; Agricultural engineering; Computer vision; Cartography; Agronomy; Geography","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":[0.0008212282,0.00122283,0.001798415,0.001982243,0.0005538915,0.001331751,0.001687021,0.00137388,0.004491405],"category_scores_gemma":[0.001476682,0.0006792882,0.001764243,0.002484706,0.0004337346,0.001335966,0.001332148,0.001330198,0.002764309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005597544,"about_ca_system_score_gemma":0.0007743798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006071056,"about_ca_topic_score_gemma":0.01179875,"domain_scores_codex":[0.9994764,0.00007327933,0.00002888694,0.0001777924,0.0001519971,0.00009166628],"domain_scores_gemma":[0.9993597,0.0002336936,0.00004715816,0.0001308692,0.0001858301,0.00004275159],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001601693,0.00008750304,0.0003136127,0.0001088755,0.00008296149,0.00004199199,0.00005298063,0.01609211,0.06137917,0.001133902,0.004043483,0.9165031],"study_design_scores_gemma":[0.00001174971,0.00007139559,0.001747623,0.00001821669,0.0000796606,0.0001479043,0.0000390539,0.961336,0.0288923,0.002428137,0.005195896,0.00003214205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01043787,0.001501567,0.982463,0.0001095866,0.0001708945,0.00005178292,0.0001832946,0.003064196,0.002017828],"genre_scores_gemma":[0.1296548,0.001339861,0.8553682,0.0003830829,0.0002205486,0.0001144899,0.0008456177,0.001318948,0.01075441],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006071056,"threshold_uncertainty_score":0.01502532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02705006949241482,"score_gpt":0.2331809464175508,"score_spread":0.206130876925136,"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."}}