{"id":"W4403421194","doi":"10.1109/tcyb.2024.3473809","title":"Deep Optimized Broad Learning System for Applications in Tabular Data Recognition","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Deep learning; Artificial intelligence; Pattern recognition (psychology); Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007543963,0.0008424653,0.000502183,0.000653077,0.0003577952,0.0009480994,0.001535465,0.0007498583,0.01036907],"category_scores_gemma":[0.002147095,0.0003669864,0.0004984019,0.0007807037,0.0004364858,0.00190241,0.001782,0.001340765,0.004321544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007524692,"about_ca_system_score_gemma":0.001293202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004412295,"about_ca_topic_score_gemma":0.008125581,"domain_scores_codex":[0.9996885,0.0000483119,0.00002180269,0.00009664938,0.00009153267,0.00005321683],"domain_scores_gemma":[0.9996087,0.00008737169,0.00003851997,0.0001031094,0.0001301648,0.00003227815],"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":[0.0004207431,0.0002102486,0.001885894,0.0002146137,0.0001002928,0.000200015,0.0001241702,0.1772988,0.02104657,0.01435713,0.03516434,0.7489772],"study_design_scores_gemma":[0.00002077473,0.00007969148,0.0004266512,0.0000185812,0.00001195715,0.00004714311,0.00002889336,0.9737449,0.007724502,0.01010202,0.007777724,0.00001721362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02668003,0.0008252935,0.9430841,0.00048062,0.000169652,0.0001384873,0.001401962,0.0225214,0.004698371],"genre_scores_gemma":[0.3348869,0.0007146001,0.6379364,0.0007373656,0.0001061558,0.0004899999,0.006641147,0.001024011,0.01746348],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01036907,"threshold_uncertainty_score":0.034688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02868902786275701,"score_gpt":0.2781412864415286,"score_spread":0.2494522585787716,"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."}}