{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00137763,0.000700842,0.0007304685,0.0005302487,0.0002510132,0.0006171963,0.0005272472,0.0006319435,0.0002622718],"category_scores_gemma":[0.002207929,0.000303383,0.0005893537,0.000501856,0.000307052,0.0004553834,0.0004366373,0.0004266856,0.00005629636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000490966,"about_ca_system_score_gemma":0.00060029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01321058,"about_ca_topic_score_gemma":0.007607586,"domain_scores_codex":[0.9996973,0.000142058,0.00002225889,0.00006084433,0.00004498543,0.00003260843],"domain_scores_gemma":[0.99927,0.0004417794,0.00006876387,0.00004915762,0.000147812,0.0000224313],"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":[0.0000928541,0.00008940523,0.006854493,0.00002045301,0.00006627269,0.00003551983,0.00002035651,0.9581763,0.00176295,0.0002044113,0.0001966578,0.03248039],"study_design_scores_gemma":[0.000003646014,0.00001701029,0.0007377418,8.378341e-7,0.000003842872,0.000001963612,0.000002873368,0.9988976,0.0002270411,0.00008290782,0.00002287811,0.000001641636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6629542,0.0003562431,0.3349196,0.0002308007,0.0000392222,0.00005568935,0.0001289403,0.0003290876,0.0009862338],"genre_scores_gemma":[0.9732553,0.00005459195,0.02614569,0.00002502135,0.00001110731,0.00004046088,0.0001395856,0.000008094677,0.0003200806],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01321058,"threshold_uncertainty_score":0.02626741,"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."}}