{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003760143,0.0002257247,0.0002422226,0.0000404383,0.001055899,0.00006296176,0.0002099961,0.0001411139,0.001581474],"category_scores_gemma":[0.0002334339,0.0001298724,0.0001600625,0.001090127,0.0002827613,0.000211909,0.0001595924,0.0003213949,0.002183835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004079798,"about_ca_system_score_gemma":0.00001652883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002566115,"about_ca_topic_score_gemma":0.00003810864,"domain_scores_codex":[0.9982159,0.0001898442,0.0004096953,0.0005043304,0.0001677882,0.0005123992],"domain_scores_gemma":[0.9992126,0.0002749415,0.0001416021,0.00009385736,0.0001065205,0.0001704404],"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":[0.0001918928,0.0006149837,0.04228629,0.0002487092,0.0001841608,0.0001608922,0.002825187,0.417319,0.2866154,0.005343165,0.005546292,0.238664],"study_design_scores_gemma":[0.00003091557,0.0001504032,0.02331162,0.0002396693,0.00005324898,0.00001605226,0.001905913,0.9033386,0.005814973,0.001827491,0.06301305,0.0002980401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9785438,0.008094066,0.001238946,0.007933002,0.002283103,0.0003507923,0.0005273783,0.0003572254,0.0006716555],"genre_scores_gemma":[0.9302602,0.004650655,0.00007934418,0.0001695893,0.001138991,0.00001578243,0.0005127597,0.000005343313,0.06316737],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4860196,"threshold_uncertainty_score":0.9993312,"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."}}