{"id":"W4385399437","doi":"10.18280/ria.370321","title":"Chili Crop Disease Prediction Using Machine Learning Algorithms","year":2023,"lang":"fr","type":"article","venue":"Revue d intelligence artificielle","topic":"Plant Physiology and Cultivation Studies","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Machine learning; Algorithm; Artificial intelligence; Crop; Agronomy; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001174006,0.0009031059,0.0008909756,0.001987963,0.0002708074,0.0007944782,0.0006360136,0.0008367698,0.0005675261],"category_scores_gemma":[0.002029087,0.0002203356,0.0007924523,0.00103886,0.0001666097,0.0006937611,0.0003138629,0.0007100696,0.0003861591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005811736,"about_ca_system_score_gemma":0.0005695451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006814718,"about_ca_topic_score_gemma":0.004900464,"domain_scores_codex":[0.9995813,0.0001068792,0.0000394977,0.0001202886,0.00008837641,0.0000637019],"domain_scores_gemma":[0.9986762,0.0007899142,0.0001601185,0.00006866112,0.000256622,0.00004834429],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003270741,0.0005251726,0.02960323,0.0001650699,0.0001940424,0.0001765984,0.00003863635,0.6783176,0.008422161,0.00059941,0.003509712,0.2781213],"study_design_scores_gemma":[0.00000409928,0.00002786131,0.002135231,0.000006943527,0.000008078865,0.00001769828,0.000008043149,0.9963581,0.0009615146,0.0002981627,0.0001691374,0.000005004537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5308542,0.004845209,0.4542394,0.0007501777,0.0001732766,0.0001959914,0.001475123,0.003720013,0.003746641],"genre_scores_gemma":[0.8707938,0.0006280944,0.1252525,0.0001154151,0.00007020672,0.00008411382,0.001922265,0.00002842441,0.001105115],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006814718,"threshold_uncertainty_score":0.0135501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07955944564305913,"score_gpt":0.2797418758609631,"score_spread":0.200182430217904,"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."}}