{"id":"W4405782385","doi":"10.20944/preprints202412.2095.v1","title":"Advanced Efficient Feature Selection Integrating Augmented Extreme Learning Machine and Particle Swarm Optimization for Predicting Nitrogen Use Efficiency and Yield in Corn","year":2024,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Particle swarm optimization; Feature selection; Yield (engineering); Feature (linguistics); Selection (genetic algorithm); Computer science; Artificial intelligence; Extreme learning machine; Machine learning; Nitrogen; Chemistry; Materials science; Composite material; Artificial neural network","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001122118,0.0003382172,0.0003307021,0.0002209052,0.0002761536,0.0002630924,0.0003137012,0.0002214168,0.000009026059],"category_scores_gemma":[0.001564973,0.0003305878,0.00007628358,0.0004121839,0.00003486643,0.0001644112,0.001828879,0.001293448,0.000005241487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001272124,"about_ca_system_score_gemma":0.0000763561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003134492,"about_ca_topic_score_gemma":0.00006595059,"domain_scores_codex":[0.997487,0.0001894504,0.0004256139,0.001242049,0.0002461619,0.0004097204],"domain_scores_gemma":[0.9987875,0.0003455823,0.0002624854,0.0003668055,0.0001215061,0.0001161775],"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":[0.00004293297,0.00005994161,0.3634442,0.0002114351,0.00002483392,0.000003263908,0.002272384,0.6253856,0.002150692,0.0001932364,0.000001883552,0.00620961],"study_design_scores_gemma":[0.000466782,0.00007152912,0.0181981,0.0006052442,0.00003307207,0.00001416029,0.0001008418,0.9762316,0.003707045,0.0002425165,0.00004506656,0.0002840075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7717277,0.0003648402,0.2261264,0.0003750949,0.0002940964,0.0006502029,0.000004100747,0.0003889607,0.0000685936],"genre_scores_gemma":[0.9833427,0.00005116968,0.01604151,0.00003605451,0.00005672517,0.0001480606,0.00002181307,0.00003558062,0.000266367],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3508461,"threshold_uncertainty_score":0.9999146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05494466930450877,"score_gpt":0.3029277410636739,"score_spread":0.2479830717591651,"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."}}