{"id":"W4367603671","doi":"10.1007/s11042-023-15548-x","title":"Rapid computer vision detection of apple diseases based on AMCFNet","year":2023,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Pattern recognition (psychology); Feature (linguistics); Computer vision; Image (mathematics); Image processing; Interference (communication); Particle swarm optimization; Channel (broadcasting); Machine learning","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":[0.0000505653,0.00008020804,0.00009981698,0.00001398582,0.0001463137,0.00003283227,0.00007753907,0.00004732086,0.00005529645],"category_scores_gemma":[0.000008070328,0.00003014949,0.00004988787,0.0003778551,0.00003936561,0.00004897704,0.00002644711,0.00004372089,0.00006419883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004201722,"about_ca_system_score_gemma":0.000002027063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002424394,"about_ca_topic_score_gemma":0.00002932176,"domain_scores_codex":[0.9994439,0.00001844013,0.0001181697,0.0001935263,0.0001131478,0.0001128571],"domain_scores_gemma":[0.9994994,0.0003008638,0.00004654842,0.00005064213,0.00003211061,0.00007044685],"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":[0.00001146894,0.0001018428,0.001225841,0.000006870841,0.000003454999,2.822328e-7,0.000012438,0.00005252336,0.1021965,0.0000398243,0.002571182,0.8937778],"study_design_scores_gemma":[0.0002414028,0.0003272012,0.7203785,0.0000187204,0.00001817788,6.387495e-7,0.00006986951,0.03156053,0.007712752,0.0001150781,0.2393899,0.0001672733],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9936264,0.000140224,0.0009368619,0.002310535,0.0001284064,0.001142852,0.0009722433,0.000345925,0.0003965197],"genre_scores_gemma":[0.9981006,0.0000553129,0.0001653585,0.0001743648,0.0004367834,0.0002074419,0.0008120754,8.34806e-7,0.00004719338],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8936105,"threshold_uncertainty_score":0.122946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01732298793500308,"score_gpt":0.2237399975905049,"score_spread":0.2064170096555018,"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."}}