{"id":"W4409944706","doi":"10.35882/jeeemi.v7i2.591","title":"Deep Vision Transformer with Tasmanian Devil Optimization for Multiclass Paddy Disease Detection and Classification for Precision Agriculture","year":2025,"lang":"en","type":"article","venue":"Journal of Electronics Electromedical Engineering and Medical Informatics","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Precision agriculture; Artificial intelligence; Agriculture; Computer science; Pattern recognition (psychology); Computer vision; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004919702,0.0001590354,0.0002483175,0.00004754595,0.0001521536,0.00006595127,0.000114927,0.0002008153,0.000003916891],"category_scores_gemma":[0.0003928902,0.00005979618,0.00007773669,0.0002730405,0.00003937268,0.0002346802,0.000007544837,0.0003178239,7.596287e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006401989,"about_ca_system_score_gemma":0.00006898243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.729617e-7,"about_ca_topic_score_gemma":0.00003821585,"domain_scores_codex":[0.9987124,0.00001499663,0.0005008436,0.0001066264,0.0003610632,0.0003040452],"domain_scores_gemma":[0.9988623,0.0003967287,0.0001660188,0.00002910011,0.0002270571,0.0003188255],"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":[0.001372344,0.0003844447,0.0001175353,0.0007712084,0.0002973786,0.000003170177,0.00037203,0.006602834,0.09418141,0.003145305,0.001450036,0.8913023],"study_design_scores_gemma":[0.001778514,0.002684652,0.002441886,0.0004031683,0.0002269137,0.00007839465,0.0001885173,0.9434657,0.001960459,0.0002584067,0.04627585,0.0002375059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2658946,0.001702182,0.7282684,0.003298957,0.000140864,0.0006303837,0.00000687056,0.00003788209,0.00001989267],"genre_scores_gemma":[0.9864405,0.003510082,0.009037321,0.0004496909,0.0003669663,0.00006737128,0.00009232629,0.000003846577,0.00003188509],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9368629,"threshold_uncertainty_score":0.2438418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00358554845067377,"score_gpt":0.2039768978056097,"score_spread":0.2003913493549359,"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."}}