{"id":"W2949244450","doi":"10.48550/arxiv.1506.02465","title":"ASlib: A Benchmark Library for Algorithm Selection","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Selection (genetic algorithm); Benchmark (surveying); Set (abstract data type); Task (project management); Exploit; Variety (cybernetics); Data mining; Range (aeronautics); Algorithm; Selection algorithm; Interface (matter); Machine learning; Artificial intelligence; Engineering","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":[0.006902956,0.004609693,0.002218251,0.006999296,0.00123669,0.004819088,0.007534048,0.003651959,0.02475331],"category_scores_gemma":[0.02797331,0.001525964,0.003059876,0.01299701,0.001216077,0.004691153,0.002723387,0.003697087,0.02216363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002530646,"about_ca_system_score_gemma":0.00501265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006369878,"about_ca_topic_score_gemma":0.008581241,"domain_scores_codex":[0.9919546,0.002622384,0.001292178,0.0008352444,0.002634841,0.0006607523],"domain_scores_gemma":[0.9798793,0.01226906,0.0009960777,0.003406139,0.002908655,0.0005407785],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00098241,0.0007197257,0.002462088,0.006221191,0.0004812487,0.0002558575,0.0001425138,0.078718,0.002432564,0.0162666,0.7097784,0.1815394],"study_design_scores_gemma":[0.001707137,0.0006339221,0.002522842,0.0009812425,0.0002586075,0.0007080226,0.0001719332,0.4858819,0.01748313,0.05371594,0.4357378,0.0001975053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"software","genre_gemma":"methods","genre_scores_codex":[0.02913514,0.01785886,0.3598062,0.003866858,0.002316319,0.002033928,0.1259267,0.3638988,0.09515714],"genre_scores_gemma":[0.09115954,0.008335539,0.5966734,0.002110587,0.0004750283,0.004733451,0.235444,0.04376084,0.01730754],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02475331,"threshold_uncertainty_score":0.08280802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06976755450058429,"score_gpt":0.200878132000168,"score_spread":0.1311105774995837,"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."}}