{"id":"W4392157946","doi":"10.1590/1809-4430-eng.agric.v44e20230097/2024","title":"SE-SWIN UNET FOR IMAGE SEGMENTATION OF MAJOR MAIZE FOLIAR DISEASES","year":2024,"lang":"en","type":"article","venue":"Engenharia Agrícola","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Agriculture","funders":"Henan Normal University","keywords":"Agronomy; Image segmentation; Segmentation; Agricultural engineering; Environmental science; Computer science; Computer vision; Biology; Engineering","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":[0.0005414286,0.0009625633,0.000472605,0.001034965,0.0002446395,0.0006566037,0.0007709012,0.0009800699,0.0015504],"category_scores_gemma":[0.0007915021,0.0002668501,0.0008517685,0.0005475607,0.0002354858,0.0009308782,0.0006336265,0.0007024465,0.00071928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007142945,"about_ca_system_score_gemma":0.0007200035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008457315,"about_ca_topic_score_gemma":0.01769446,"domain_scores_codex":[0.9997787,0.0000278765,0.00001260002,0.00009192059,0.00004667563,0.00004215546],"domain_scores_gemma":[0.999813,0.00004840398,0.00002530134,0.00003622588,0.00006143839,0.00001573424],"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":[0.0008081473,0.0002872081,0.007904896,0.0002765427,0.0002422741,0.0003974562,0.000119884,0.2776335,0.09045848,0.002511631,0.008853828,0.6105062],"study_design_scores_gemma":[0.000009199385,0.00007726668,0.002175143,0.000009823412,0.00002719884,0.0001111095,0.00001906715,0.9745876,0.02000517,0.0008747617,0.002092488,0.00001111946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2500696,0.001967516,0.728852,0.0005030728,0.0001628971,0.0002107762,0.002060391,0.01007179,0.006102107],"genre_scores_gemma":[0.7415376,0.0008334392,0.2391499,0.0004393397,0.00005852957,0.0001282632,0.006874022,0.0004023423,0.01057654],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008457315,"threshold_uncertainty_score":0.01681614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01263828281300992,"score_gpt":0.2459859531144964,"score_spread":0.2333476703014865,"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."}}