{"id":"W4382560805","doi":"10.3390/s23136015","title":"Crop Disease Identification by Fusing Multiscale Convolution and Vision Transformer","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Convolutional neural network; Artificial intelligence; Computer science; Residual; Deep learning; Pattern recognition (psychology); Adaptability; Machine learning; Convolution (computer science); Artificial neural network; Algorithm","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":[0.00008413265,0.00007199276,0.00006233191,0.000007538308,0.0002081961,0.0000552434,0.00004088339,0.00004312002,0.0000446841],"category_scores_gemma":[0.00001429638,0.00002577062,0.00003633222,0.0002496403,0.00003864436,0.00009951251,0.000008487126,0.00004042451,0.000111027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007378933,"about_ca_system_score_gemma":0.00000114124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007555812,"about_ca_topic_score_gemma":0.00007636486,"domain_scores_codex":[0.9994098,0.00002827053,0.0001065409,0.0001915605,0.0001162062,0.0001476173],"domain_scores_gemma":[0.999796,0.00003982132,0.00002685652,0.00002345409,0.00002477888,0.00008913934],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00001474893,0.00002345224,0.004351374,0.000005784757,0.000003189102,0.000002231215,0.00009913128,0.00001222167,0.9497431,0.00002616292,0.009390346,0.03632826],"study_design_scores_gemma":[0.0001192669,0.00003776655,0.9410754,0.00001753576,0.00001516753,0.000001688724,0.0003501909,0.001361864,0.01041045,0.0001402087,0.04631863,0.0001517943],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963831,0.0001241576,0.000002567836,0.002990971,0.0001023799,0.0001255278,0.00004993971,0.0001084007,0.0001129543],"genre_scores_gemma":[0.9976121,0.0001242198,0.000003966948,0.00006796568,0.0001110391,0.000004722732,0.0002759464,5.911781e-7,0.00179943],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9393327,"threshold_uncertainty_score":0.1601297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009708834749210223,"score_gpt":0.2346685095810885,"score_spread":0.2249596748318783,"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."}}