{"id":"W4405414691","doi":"10.1007/s00371-024-03729-0","title":"Dynamic text prompt joint multimodal features for accurate plant disease image captioning","year":2024,"lang":"en","type":"article","venue":"The Visual Computer","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"Natural Science Foundation of Hebei Province; National Natural Science Foundation of China","keywords":"Closed captioning; Computer science; Feature (linguistics); Artificial intelligence; Plant disease; Image (mathematics); Machine learning; Natural language processing","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":[0.0003680032,0.0002356114,0.0001688469,0.0001292444,0.0004154758,0.0009320166,0.0008475473,0.00004603886,0.00001144383],"category_scores_gemma":[0.00003371384,0.0001601034,0.0001552179,0.0002909602,0.00007629937,0.0003492505,0.0004528986,0.0003519241,0.0002317481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007003386,"about_ca_system_score_gemma":0.00009580062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008537691,"about_ca_topic_score_gemma":0.000007154275,"domain_scores_codex":[0.9984177,0.0001081422,0.0002512003,0.0006093192,0.0002433891,0.0003702912],"domain_scores_gemma":[0.998844,0.0003153855,0.00007120727,0.0005565364,0.0000709201,0.0001419302],"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":[0.000229115,0.0008761086,0.0002976048,0.001132368,0.0005314003,0.0001858635,0.01219652,0.1085924,0.02404183,0.2835054,0.02998492,0.5384265],"study_design_scores_gemma":[0.0001963548,0.00007149208,0.02711898,0.00008081491,0.00002012356,0.0000240358,0.000006420948,0.968172,0.00008731084,0.002397799,0.001603786,0.0002209401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05832147,0.0002094487,0.9308178,0.008406746,0.0005519024,0.0008158161,0.0000446065,0.0007840012,0.00004821505],"genre_scores_gemma":[0.8867386,0.000003247899,0.1120273,0.0003936298,0.0002795582,0.0002395661,0.00005953158,0.00003249367,0.0002260122],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8595796,"threshold_uncertainty_score":0.8987458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01490606545697368,"score_gpt":0.3184719595735442,"score_spread":0.3035658941165705,"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."}}