{"id":"W4416799340","doi":"10.1109/icmic66299.2025.11257783","title":"Enhancing Machine Learning Performance Through Quantile Binning for Resource Forecasting","year":2025,"lang":"","type":"article","venue":"","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Convergent Manufacturing Technologies (Canada)","funders":"","keywords":"Benchmark (surveying); Artificial neural network; Perceptron; Predictive modelling; Multilayer perceptron; Data pre-processing; Hyperparameter; Preprocessor; Regression; Resource (disambiguation)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.005228397,0.0005139991,0.0007368913,0.0004992621,0.003234597,0.0008390469,0.001490129,0.0002981242,0.000454894],"category_scores_gemma":[0.00491832,0.0004468042,0.0003983691,0.003000721,0.0002659055,0.0006835687,0.0008449086,0.0007879491,0.00005720146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001349018,"about_ca_system_score_gemma":0.0002676824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001774256,"about_ca_topic_score_gemma":0.00008006701,"domain_scores_codex":[0.9945533,0.0001581102,0.002025018,0.001410193,0.0007792906,0.001074048],"domain_scores_gemma":[0.9930408,0.004431006,0.0008131408,0.0009894053,0.0006027229,0.000122929],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003935839,0.0003158116,0.01519138,0.0007439618,0.0001479298,0.000003990063,0.003620261,0.01506956,0.006209973,0.1082482,0.02683568,0.8232197],"study_design_scores_gemma":[0.0003430238,0.0002325677,0.0000605993,0.0006902681,0.00004520118,0.000008561145,0.001094231,0.5679036,0.02168873,0.005800911,0.4018053,0.0003270015],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07948483,0.001001074,0.8276178,0.001927683,0.0003696255,0.001318005,0.00002609248,0.0005063633,0.08774853],"genre_scores_gemma":[0.7636664,0.0001526489,0.1932537,0.0005825955,0.0001547849,0.0002358473,0.00002289776,0.00004855981,0.04188264],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8228927,"threshold_uncertainty_score":0.9997984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1312351460301739,"score_gpt":0.3839412775479824,"score_spread":0.2527061315178084,"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."}}