{"id":"W2897647221","doi":"10.48550/arxiv.1810.10158","title":"Randomized Gradient Boosting Machine","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Booth University College","funders":"Office of Naval Research; National Science Foundation","keywords":"Boosting (machine learning); Computer science; Machine learning; Artificial intelligence; Gradient boosting; Algorithm; Fraction (chemistry)","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"],"consensus_categories":[],"category_scores_codex":[0.00107595,0.0003925356,0.0006920942,0.0002850621,0.0002782829,0.0001960914,0.002106117,0.0002188751,0.00006968251],"category_scores_gemma":[0.0002171505,0.0003852892,0.0004396771,0.0004341803,0.0002203621,0.0001966227,0.002804718,0.0008785868,0.000214475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001116974,"about_ca_system_score_gemma":0.0001155012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004999657,"about_ca_topic_score_gemma":0.00001520452,"domain_scores_codex":[0.9973351,0.000554515,0.0002852559,0.001253729,0.0001276622,0.0004436867],"domain_scores_gemma":[0.9976234,0.0003147778,0.0004016118,0.001296638,0.0001526871,0.0002108597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005286208,0.0004174107,0.00344306,0.0003680271,0.00092638,0.001721153,0.002169479,0.3122714,0.0000174405,0.6585065,0.002663806,0.01220915],"study_design_scores_gemma":[0.01989995,0.00003449157,0.00007313112,0.00009715556,0.00006923977,0.0000097388,0.000007565603,0.936941,0.00002651468,0.04147135,0.0009405463,0.0004293454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07670027,0.0001608946,0.9090928,0.0002588103,0.001568071,0.0003902903,0.000006516797,0.0006317433,0.0111906],"genre_scores_gemma":[0.985429,0.0001009618,0.009233337,0.0001471853,0.0002783066,0.000001531291,0.00002035626,0.00002319414,0.004766095],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9087288,"threshold_uncertainty_score":0.9998599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04777259867698978,"score_gpt":0.1936371406089367,"score_spread":0.145864541931947,"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."}}