{"id":"W4389041634","doi":"10.1109/isncc58260.2023.10323932","title":"Detection of Plant Diseases in an Industrial Greenhouse: Development, Validation & Exploitation","year":2023,"lang":"en","type":"article","venue":"","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Collège de Maisonneuve; Laboratoire Recherche Informatique Maisonneuve","funders":"","keywords":"Greenhouse; Computer science; Object detection; Robustness (evolution); Context (archaeology); Hyperparameter; Artificial intelligence; Machine learning; Pattern recognition (psychology)","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":[0.0001110118,0.0000590038,0.00007323684,0.00002550615,0.00005696839,0.00001936477,0.00006255475,0.00006110766,0.00007859751],"category_scores_gemma":[0.0000383057,0.00002063415,0.00002068875,0.0005746021,0.000007901219,0.0001965897,0.0000166927,0.00003811579,0.00003831615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001399409,"about_ca_system_score_gemma":0.000005710818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001439719,"about_ca_topic_score_gemma":0.005702565,"domain_scores_codex":[0.9994157,0.00004426741,0.0001685836,0.0001291545,0.0001371707,0.0001051439],"domain_scores_gemma":[0.9997938,0.00006616103,0.00005044457,0.00001931763,0.00003164629,0.00003863428],"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":[0.00007047804,0.0002595211,0.1810917,0.00001308919,0.000007356419,0.000001672257,0.0004243553,0.00002086258,0.6000451,0.00004513779,0.001570738,0.2164501],"study_design_scores_gemma":[0.000225031,0.00008491566,0.9436679,0.00002718263,0.000003444616,5.254862e-7,0.0004781691,0.0000138347,0.04323002,0.0001158687,0.0120492,0.0001038514],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994882,0.000005142552,0.000006843793,0.0001003725,0.0001020727,0.0001153091,0.00002452721,0.00009997206,0.00005755702],"genre_scores_gemma":[0.99856,0.0000113364,0.00001561746,0.00002033855,0.0001838437,0.00002008961,0.001044892,3.917771e-7,0.0001435382],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7625763,"threshold_uncertainty_score":0.3182165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08302874964671374,"score_gpt":0.2482984300604388,"score_spread":0.1652696804137251,"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."}}