{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009691469,0.001330566,0.001174691,0.001041145,0.0006378827,0.002551263,0.003145739,0.001825866,0.00274769],"category_scores_gemma":[0.05269326,0.0007479655,0.0009471948,0.001377634,0.001181784,0.005162789,0.001170497,0.002633099,0.0007072092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002246482,"about_ca_system_score_gemma":0.002219641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003162228,"about_ca_topic_score_gemma":0.003309202,"domain_scores_codex":[0.9912549,0.004175616,0.0004095988,0.001595159,0.001995509,0.0005691741],"domain_scores_gemma":[0.9475204,0.03169342,0.00253901,0.01535199,0.002573087,0.0003221911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006142819,0.0007187041,0.01140316,0.0002277654,0.00009688663,0.00007257429,0.0001289052,0.8270511,0.00638398,0.01554001,0.003032681,0.1347299],"study_design_scores_gemma":[0.00002263431,0.000115747,0.0008026714,0.00001147967,0.000006925282,0.000022039,0.00001454154,0.9912546,0.002282893,0.004943963,0.0005105443,0.00001190299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.457731,0.002634692,0.5170919,0.001931901,0.0001220685,0.0002874482,0.000814732,0.008396483,0.01098985],"genre_scores_gemma":[0.9160483,0.0002751896,0.08202328,0.0001195582,0.00004380462,0.0001497972,0.0004664155,0.0003554793,0.0005182028],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009691469,"threshold_uncertainty_score":0.05125397,"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."}}