{"id":"W2123046986","doi":"","title":"Performance Prediction and Automated Tuning of Randomized and Parametric Algorithms: An Initial Investigation ∗","year":2006,"lang":"en","type":"article","venue":"","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Algorithm; Machine learning; Parametric statistics; Randomized algorithm; Artificial intelligence; Mathematics; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.0007358631,0.00008642285,0.0002174632,0.0002136475,0.00009555295,0.00009443424,0.00009502427,0.00004494551,0.000002662783],"category_scores_gemma":[0.00007998759,0.00006990846,0.00001804363,0.0003033648,0.0001141709,0.0006165264,0.00005223949,0.00008391887,6.008808e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006096873,"about_ca_system_score_gemma":0.00001985989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003849796,"about_ca_topic_score_gemma":0.000001990233,"domain_scores_codex":[0.9990907,0.0001672779,0.0002644593,0.0002073233,0.0001625124,0.0001077648],"domain_scores_gemma":[0.9995031,0.0001474411,0.0001168683,0.0001219952,0.00005801001,0.00005259798],"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":[0.00160956,0.0003234293,0.1828163,0.000419314,0.0001456095,0.00001487617,0.00519281,0.03123772,0.004196003,0.03438969,0.0004264101,0.7392283],"study_design_scores_gemma":[0.009776414,0.00009963671,0.02986807,0.0000176501,0.000009499071,0.00001856445,0.000008283617,0.958961,0.0006020989,0.0005577932,0.000008722626,0.00007226145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8603707,0.00007420421,0.1380608,0.0000828806,0.0000809663,0.0001348931,9.747848e-7,0.0004388822,0.000755719],"genre_scores_gemma":[0.9202325,0.00002150709,0.0795886,0.00002477775,0.00005128377,0.000007208882,0.000009493254,0.000003884449,0.00006074086],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9277233,"threshold_uncertainty_score":0.2850784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009951827015683389,"score_gpt":0.2438050837366216,"score_spread":0.2338532567209382,"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."}}