{"id":"W109784087","doi":"10.1007/978-3-540-74970-7_50","title":"SATzilla-07: The Design and Analysis of an Algorithm Portfolio for SAT","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Formal Methods in Verification","field":"Computer Science","cited_by":108,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Solver; Leverage (statistics); Portfolio; Computer science; Class (philosophy); Set (abstract data type); Algorithm; Theoretical computer science; Machine learning; Artificial intelligence; Programming language","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":[0.004569522,0.001184135,0.0009473952,0.001153859,0.000723173,0.003983445,0.004349275,0.001760583,0.01991929],"category_scores_gemma":[0.01243663,0.001358828,0.002102431,0.001153107,0.001565728,0.004765718,0.003087909,0.002723448,0.0053595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00227963,"about_ca_system_score_gemma":0.003351796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009942416,"about_ca_topic_score_gemma":0.002013042,"domain_scores_codex":[0.9954477,0.001813307,0.000337771,0.0006496274,0.001296899,0.0004546891],"domain_scores_gemma":[0.9961978,0.001960194,0.0002278397,0.001058282,0.0004317777,0.0001240204],"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":[0.0009468522,0.0004618276,0.002808074,0.0008158278,0.000272067,0.0001627272,0.0002714473,0.1761192,0.01517077,0.4246528,0.03778004,0.3405383],"study_design_scores_gemma":[0.0001804038,0.0002464949,0.0002812194,0.000102383,0.00008557621,0.0001589472,0.00004293626,0.8189465,0.01848427,0.1315288,0.0299079,0.00003455245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01091166,0.0002445787,0.9625624,0.0004069098,0.0001419031,0.0004093205,0.0004194131,0.01287666,0.0120272],"genre_scores_gemma":[0.1726127,0.0002722213,0.8120479,0.0004640331,0.00009742845,0.000798324,0.00147966,0.003663982,0.008563857],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01991929,"threshold_uncertainty_score":0.06663674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04707438216720248,"score_gpt":0.3155200354914582,"score_spread":0.2684456533242557,"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."}}