{"id":"W4405583035","doi":"10.37256/cm.5420244481","title":"An Experimental Analysis of Traditional Machine Learning Algorithms for Maize Yield Prediction","year":2024,"lang":"en","type":"article","venue":"Contemporary Mathematics","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Deutscher Akademischer Austauschdienst; International Development Research Centre; Styrelsen för Internationellt Utvecklingssamarbete","keywords":"Mathematics; Algorithm; Yield (engineering); Machine learning; Artificial intelligence; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001847736,0.00010177,0.0002106861,0.00002376425,0.00008496285,0.00005261304,0.000104703,0.00006450233,0.0002497729],"category_scores_gemma":[0.00002127689,0.0000380865,0.0002022618,0.0003879846,0.00002554395,0.0001859314,0.00001124537,0.00007008263,0.000002824554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001027142,"about_ca_system_score_gemma":0.000006447537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001817855,"about_ca_topic_score_gemma":0.00001202766,"domain_scores_codex":[0.9992976,0.00001986714,0.0002564806,0.0001784961,0.0001550322,0.00009256286],"domain_scores_gemma":[0.9994651,0.0003485785,0.0000637638,0.00003605065,0.00003501426,0.00005151409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004485161,0.001352353,0.003560691,0.0001517673,0.00103166,0.00001102305,0.002579823,0.0001119518,0.9615565,0.01023153,0.01123269,0.008135139],"study_design_scores_gemma":[0.0006303383,0.005306472,0.02581978,0.0005257538,0.001599557,0.00003568633,0.01125596,0.7413497,0.1533826,0.006629658,0.05238664,0.001077922],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9936517,0.001530782,0.001174315,0.0002912005,0.0002121896,0.0003195462,0.0008188689,0.0001815236,0.001819801],"genre_scores_gemma":[0.9971392,0.000007443798,0.00081647,0.00002951181,0.000280825,0.00003774833,0.001276537,0.000001295905,0.0004109785],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.808174,"threshold_uncertainty_score":0.2734838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09866828420263846,"score_gpt":0.271002388836738,"score_spread":0.1723341046340995,"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."}}