{"id":"W4391506137","doi":"10.48550/arxiv.2402.00645","title":"Spectrally Transformed Kernel Regression","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of Naval Research; Defense Advanced Research Projects Agency; Institute for Catastrophic Loss Reduction; National Science Foundation","keywords":"Kernel regression; Regression; Kernel (algebra); Mathematics; Statistics; Computer science; Artificial intelligence; Econometrics; Discrete mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002425162,0.0008695783,0.0009833018,0.0008183826,0.0004385633,0.001307153,0.001328791,0.001171626,0.002760275],"category_scores_gemma":[0.01413882,0.0004741478,0.0008196059,0.001226404,0.001696365,0.003175217,0.002111556,0.002743862,0.001810548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007289203,"about_ca_system_score_gemma":0.00118117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001845769,"about_ca_topic_score_gemma":0.001816876,"domain_scores_codex":[0.9979832,0.0007186804,0.00007266071,0.0004804094,0.0006137859,0.0001311746],"domain_scores_gemma":[0.995007,0.002125128,0.0004343393,0.001386819,0.0009150209,0.0001316906],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002041258,0.0001393642,0.00190821,0.0002486325,0.0001130228,0.0001596043,0.0001993447,0.4995986,0.01370139,0.252491,0.008655469,0.2225813],"study_design_scores_gemma":[0.000007731814,0.00001993611,0.0002295476,0.000010013,0.000006207214,0.00004357496,0.00001470656,0.9564601,0.001941234,0.03935772,0.001897087,0.0000122603],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00442275,0.0001075998,0.9939955,0.0001336751,0.00002156413,0.00001587581,0.00005838907,0.0003784509,0.0008661876],"genre_scores_gemma":[0.4764632,0.0007984994,0.5112642,0.000463575,0.0002359165,0.0001653519,0.001182746,0.0007034731,0.00872292],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002760275,"threshold_uncertainty_score":0.01282567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05881918905207582,"score_gpt":0.1967955850008797,"score_spread":0.1379763959488039,"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."}}